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Dr. Fuyuan Xiao
School of Computer and Information Science, Southwest University, No.2 Tiansheng Road, BeiBei District, Chongqing 400715, China

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Research Keywords & Expertise

0 Complex Event Processing
0 fuzzy sets
0 information fusion
0 Evidence theory
0 Uncertain information modelling and processing

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Journal article
Published: 12 June 2021 in Renewable Energy
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The peak period of an energy-generating wave is one of the most important parameters that describe the spectral shape of the oceanic wave, as this indicates the duration for which the waves prevail with respect to their maximum extractable energy. In this paper, a half-hourly peak wave energy period (TP) forecast model is constructed using a suite of statistically significant lagged inputs based on the partial auto-correlation function with an extreme learning machine model developed and its predictive utility is benchmarked against deep learning models, i.e., convolutional neural network (CNN/CovNet) and recurrent neural network (RNN) models and other traditional M5tree, Conditional Maximization based Multiple Linear Regression (MLR-ECM) and MLR models. The objective model (ELM) vs. the comparison models (CNN, RNN, M5tree, MLR-ECM, and MLR) were trained and validated independently on the test dataset obtained from coastal zones of eastern Australia that have a high potential for implementation of wave energy generation systems. The outcomes ascertain that the ELM model can generate significantly accurate predictions of the half-hourly peak wave energy period, providing a good level of accuracy relative to deep learning models in selected coastal study zones. The study establishes the practical usefulness of the ELM model as being a noteworthy methodology for the applications in renewable and sustainable energy resource management systems.

ACS Style

Mumtaz Ali; Ramendra Prasad; Yong Xiang; Adarsh Sankaran; Ravinesh C. Deo; Fuyuan Xiao; Shuyu Zhu. Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia. Renewable Energy 2021, 177, 1031 -1044.

AMA Style

Mumtaz Ali, Ramendra Prasad, Yong Xiang, Adarsh Sankaran, Ravinesh C. Deo, Fuyuan Xiao, Shuyu Zhu. Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia. Renewable Energy. 2021; 177 ():1031-1044.

Chicago/Turabian Style

Mumtaz Ali; Ramendra Prasad; Yong Xiang; Adarsh Sankaran; Ravinesh C. Deo; Fuyuan Xiao; Shuyu Zhu. 2021. "Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia." Renewable Energy 177, no. : 1031-1044.

Research article
Published: 24 February 2021 in Journal of Healthcare Engineering
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In decision-making systems, how to measure uncertain information remains an open issue, especially for information processing modeled on complex planes. In this paper, a new complex entropy is proposed to measure the uncertainty of a complex-valued distribution (CvD). The proposed complex entropy is a generalization of Gini entropy that has a powerful capability to measure uncertainty. In particular, when a CvD reduces to a probability distribution, the complex entropy will degrade into Gini entropy. In addition, the properties of complex entropy, including the nonnegativity, maximum and minimum entropies, and boundedness, are analyzed and discussed. Several numerical examples illuminate the superiority of the newly defined complex entropy. Based on the newly defined complex entropy, a multisource information fusion algorithm for decision-making is developed. Finally, we apply the decision-making algorithm in a medical diagnosis problem to validate its practicability.

ACS Style

Fuyuan Xiao; Xiao-Guang Yue. Complex Entropy and Its Application in Decision-Making for Medical Diagnosis. Journal of Healthcare Engineering 2021, 2021, 1 -10.

AMA Style

Fuyuan Xiao, Xiao-Guang Yue. Complex Entropy and Its Application in Decision-Making for Medical Diagnosis. Journal of Healthcare Engineering. 2021; 2021 ():1-10.

Chicago/Turabian Style

Fuyuan Xiao; Xiao-Guang Yue. 2021. "Complex Entropy and Its Application in Decision-Making for Medical Diagnosis." Journal of Healthcare Engineering 2021, no. : 1-10.

Journal article
Published: 27 January 2021 in Sensors
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Multisource information fusion has received much attention in the past few decades, especially for the smart Internet of Things (IoT). Because of the impacts of devices, the external environment, and communication problems, the collected information may be uncertain, imprecise, or even conflicting. How to handle such kinds of uncertainty is still an open issue. Complex evidence theory (CET) is effective at disposing of uncertainty problems in the multisource information fusion of the IoT. In CET, however, how to measure the distance among complex basis belief assignments (CBBAs) to manage conflict is still an open issue, which is a benefit for improving the performance in the fusion process of the IoT. In this paper, therefore, a complex Pignistic transformation function is first proposed to transform the complex mass function; then, a generalized betting commitment-based distance (BCD) is proposed to measure the difference among CBBAs in CET. The proposed BCD is a generalized model to offer more capacity for measuring the difference among CBBAs. Additionally, other properties of the BCD are analyzed, including the non-negativeness, nondegeneracy, symmetry, and triangle inequality. Besides, a basis algorithm and its weighted extension for multi-attribute decision-making are designed based on the newly defined BCD. Finally, these decision-making algorithms are applied to cope with the medical diagnosis problem under the smart IoT environment to reveal their effectiveness.

ACS Style

Fuyuan Xiao. Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT. Sensors 2021, 21, 840 .

AMA Style

Fuyuan Xiao. Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT. Sensors. 2021; 21 (3):840.

Chicago/Turabian Style

Fuyuan Xiao. 2021. "Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT." Sensors 21, no. 3: 840.

Research article
Published: 19 January 2021 in International Journal of Intelligent Systems
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An open issue of the Dempster combination rule is the conflicting management, which is very important in multisource data fusion, such as group decision making and target recognition. To address this issue, an improved method to generate basic probability assignment is presented. Then, a new combination method to assign the conflicting mass function without the normalization is proposed to handle a highly conflicting environment. Compared with other methods, this proposed method is convenient in computing and has better accuracy to predict potential possibilities especially when disposing of extreme status. Some numerical examples and real benchmark data collected in UCI database are illustrated to verify the validity and rationality of the proposed method.

ACS Style

Yuanpeng He; Fuyuan Xiao. Conflicting management of evidence combination from the point of improvement of basic probability assignment. International Journal of Intelligent Systems 2021, 36, 1914 -1942.

AMA Style

Yuanpeng He, Fuyuan Xiao. Conflicting management of evidence combination from the point of improvement of basic probability assignment. International Journal of Intelligent Systems. 2021; 36 (5):1914-1942.

Chicago/Turabian Style

Yuanpeng He; Fuyuan Xiao. 2021. "Conflicting management of evidence combination from the point of improvement of basic probability assignment." International Journal of Intelligent Systems 36, no. 5: 1914-1942.

Article
Published: 18 November 2020 in Applied Intelligence
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The generalized evidence theory (GET) is an efficient mathematical methodology to deal with multi-source information fusion problems. The GET has the capability of handling uncertain problems even in the open world. In real world applications, some noise or other disturbance often makes the multi-source information have uncertainty. Thus, how to reliably generate the generalized basic probability assignment (GBPA) is a key problem of GET, especially under the noisy environment. Therefore, in this paper, we propose a novel approach to generate GBPA with high robustness by using a cluster method. In this way, the proposed model has the ability to correctly identify the target even under a noisy environment. In particular, the k-means++ algorithm based on triangular fuzzy number is applied to build the GBPA generation model. According to the proposed GBPA generation model, the related similarity degree is calculated for each test instance. After resolving the existing conflicts, the final GBPAs are obtained by using the generalized combination rule. To demonstrate the effectiveness of the proposed method, we compare the proposed approach with related work in the applications of classification and fault diagnosis problems, respectively. Through experimental analysis, it is verified that the proposed approach has the best robustness to generate the GBPAs and maintain a high recognition rate under both noisy and noiseless environments.

ACS Style

Yi Fan; Tianshuo Ma; Fuyuan Xiao. An improved approach to generate generalized basic probability assignment based on fuzzy sets in the open world and its application in multi-source information fusion. Applied Intelligence 2020, 1 -18.

AMA Style

Yi Fan, Tianshuo Ma, Fuyuan Xiao. An improved approach to generate generalized basic probability assignment based on fuzzy sets in the open world and its application in multi-source information fusion. Applied Intelligence. 2020; ():1-18.

Chicago/Turabian Style

Yi Fan; Tianshuo Ma; Fuyuan Xiao. 2020. "An improved approach to generate generalized basic probability assignment based on fuzzy sets in the open world and its application in multi-source information fusion." Applied Intelligence , no. : 1-18.

Conference paper
Published: 11 November 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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The world has entered an era of globalization, which represents the explosion of information. The application and development of big data technology make the scale of medical data geometric growth. People cannot intuitively see the correlation and the implicit relationship between complex medical data, which leads to the situation of more data and less knowledge. By using Spark and a variety of data pre-processing techniques and machine learning related algorithms, we implemented a platform which could help patients recommend more accurate treatment plans, help doctors analyze the relationship between diseases, and provide more natural results through a visual interface. Besides, we proposed a distributed frequent itemset mining algorithm (DSDFIM) based on the adjacency list and Spark. After evaluating, the proposed algorithm could reduce data transportation once between main memory and secondary storage, and improved the speed of data processing through distributed computing, compared with the classic algorithm. Meanwhile, it could solve the problem of merging frequent itemset of the same item under different independent paths.

ACS Style

Mingxue Zhang; Fuyuan Xiao. A Hybrid Distributed Frequent Itemset Mining Method with Its Application in Medical Diagnosis. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 394 -403.

AMA Style

Mingxue Zhang, Fuyuan Xiao. A Hybrid Distributed Frequent Itemset Mining Method with Its Application in Medical Diagnosis. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():394-403.

Chicago/Turabian Style

Mingxue Zhang; Fuyuan Xiao. 2020. "A Hybrid Distributed Frequent Itemset Mining Method with Its Application in Medical Diagnosis." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 394-403.

Research article
Published: 30 October 2020 in International Journal of Intelligent Systems
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In multisource information fusion, the weight assignment is critical to improve the fusion performance, especially under uncertainty and conflict situations. As for uncertainty, the probability distribution is a desirable method to model uncertainty. In this context, a novel dynamic weight allocation method is first proposed, which considers three situations: nonconflict, general conflict, and high conflict. In particular, according to the degree of conflict, different weight allocations are considered by means of generalized information quality and negation operation. After that, a novel dynamic weight allocation method is proposed that contains three subalgorithms: NonC algorithm, BNC algorithm, and BIC algorithm. In addition, a novel multisource information fusion method is presented based on the newly designed dynamic weight allocation method. Besides, some numerical examples illustrate its feasibility. Finally, an application in target recognition demonstrates the practicability of the proposed fusion method.

ACS Style

Yuting Li; Fuyuan Xiao. A novel dynamic weight allocation method for multisource information fusion. International Journal of Intelligent Systems 2020, 36, 736 -756.

AMA Style

Yuting Li, Fuyuan Xiao. A novel dynamic weight allocation method for multisource information fusion. International Journal of Intelligent Systems. 2020; 36 (2):736-756.

Chicago/Turabian Style

Yuting Li; Fuyuan Xiao. 2020. "A novel dynamic weight allocation method for multisource information fusion." International Journal of Intelligent Systems 36, no. 2: 736-756.

Article
Published: 08 October 2020 in International Journal of Fuzzy Systems
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Granular computing (GrC) is an essential tool to solve human real problem since the information granules is close to human perception schemes. In GrC, both classification accuracy and interpretability play significant roles. Fuzzy rule (FR) based classification systems are effective methods solving this problem. However, the accuracy of FR may be decreased when solving some complex application. In this paper, a novel model called FR–KDE integrating the FR and kernel density estimation (KDE) in the framework of Dempster–Shafer evidence theory is proposed to deal with the classification problem. By fusing the result of FR and KDE via the Dempster’s combination rule, it can reduce the uncertainty of FR and obtain better accuracy. To illustrate the effect of the FR–KDE approach, it is applied to the medical data classification problem. Experimentally, the results demonstrate that the FR–KDE method is effective in handling biomedical data classification problems.

ACS Style

Xingjian Song; Bowen Qin; Fuyuan Xiao. FR–KDE: A Hybrid Fuzzy Rule-Based Information Fusion Method with its Application in Biomedical Classification. International Journal of Fuzzy Systems 2020, 23, 392 -404.

AMA Style

Xingjian Song, Bowen Qin, Fuyuan Xiao. FR–KDE: A Hybrid Fuzzy Rule-Based Information Fusion Method with its Application in Biomedical Classification. International Journal of Fuzzy Systems. 2020; 23 (2):392-404.

Chicago/Turabian Style

Xingjian Song; Bowen Qin; Fuyuan Xiao. 2020. "FR–KDE: A Hybrid Fuzzy Rule-Based Information Fusion Method with its Application in Biomedical Classification." International Journal of Fuzzy Systems 23, no. 2: 392-404.

Journal article
Published: 24 August 2020 in Engineering Applications of Artificial Intelligence
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Intuitionistic fuzzy sets (IFS) are widely used in multi-attribute decision-making (MADM) because of its strong ability to express uncertainty in terms of membership degree, non-membership degree and hesitancy degree. Additionally, Z-number is a novel two-dimension framework to handle uncertainty problems by introducing the reliability of expert evaluation. However, a simple index in the framework of Z-number is not enough to express the evaluation of experts. In order to integrate the uncertainty and reliability expressions of IFS, inspired by Z-number, we propose a two-dimensional intuitionistic fuzzy set (TDIFS) model in this paper. In TDIFS model, the first dimensionality is the evaluation data from experts with regard to attributes, and the second dimensionality represents the reliability of expert in terms of the first component of TDIFS. Moreover, for each dimensionality, it is expressed as an ordered pair of intuitionistic fuzzy set, which can carry more information than a simple index. Furthermore, a novel combination rule is proposed for fusing TDIFSs. The TDIFS combination rule fully integrates expert evaluation and expert reliability, where it can reduce the uncertainty during combination process, so that more convincing results can be obtained. In addition, a new MADM method is proposed based on TDIFS model and TDIFS combination rule. Through comparing with the existing methods in an application of pattern recognition, it is demonstrated that the proposed MADM method is more effective, which can achieve higher robustness and better recognition results.

ACS Style

Yi Fan; Fuyuan Xiao. TDIFS: Two dimensional intuitionistic fuzzy sets. Engineering Applications of Artificial Intelligence 2020, 95, 103882 .

AMA Style

Yi Fan, Fuyuan Xiao. TDIFS: Two dimensional intuitionistic fuzzy sets. Engineering Applications of Artificial Intelligence. 2020; 95 ():103882.

Chicago/Turabian Style

Yi Fan; Fuyuan Xiao. 2020. "TDIFS: Two dimensional intuitionistic fuzzy sets." Engineering Applications of Artificial Intelligence 95, no. : 103882.

Research article
Published: 18 August 2020 in International Journal of Intelligent Systems
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For the sake of great ability of handling uncertain information, Dempster‐Shafer evidence theory is extensively used in information fusion. Nevertheless, when there exists highly inconsistent evidences, using classical Dempster's combination rule may lead to counter‐intuitive results. To address this issue, a new conflicting evidences combination method based on distance function and Tsallis entropy is proposed. Numerical examples are used to illustrate the feasibility and efficiency of the proposed method. Further, an fault diagnosis problem is used as an example to show the effectiveness and superiority of the proposed method. The proposed method outperforms other methods that the proposed method recognize the target by the probability 99.49%, which is higher than other methods.

ACS Style

Hanwen Li; Fuyuan Xiao. A method for combining conflicting evidences with improved distance function and Tsallis entropy. International Journal of Intelligent Systems 2020, 35, 1 .

AMA Style

Hanwen Li, Fuyuan Xiao. A method for combining conflicting evidences with improved distance function and Tsallis entropy. International Journal of Intelligent Systems. 2020; 35 (11):1.

Chicago/Turabian Style

Hanwen Li; Fuyuan Xiao. 2020. "A method for combining conflicting evidences with improved distance function and Tsallis entropy." International Journal of Intelligent Systems 35, no. 11: 1.

Journal article
Published: 14 August 2020 in IEEE Transactions on Fuzzy Systems
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In real applications of artificial and intelligent decision-making systems, how to represent the knowledge involved with uncertain information is still an open issue. The negation method has great significance to address this issue from another perspective. However, it has the limitation that can be used only for the negation of the probability distribution. In this paper, therefore, we propose a generalized model of the traditional one, so that it can has more powerful capability to represent the knowledge and uncertainty measure. In particular, we first define a vector representation of complex-valued distribution. Then, an entropy measure is proposed for the complex-valued distribution, called X entropy. In this context, a transformation function to acquire the negation of the complex-valued distribution is exploited on the basis of the newly defined X entropy. Afterwards, the properties of this negation function are analyzed and investigated, as well as some special cases. Finally, we study the negation function on the view from the X entropy. It is verified that the proposed negation method for the complex-valued distribution is a scheme with a maximal entropy.

ACS Style

Fuyuan Xiao. On the maximum entropy negation of a complex-valued distribution. IEEE Transactions on Fuzzy Systems 2020, PP, 1 -1.

AMA Style

Fuyuan Xiao. On the maximum entropy negation of a complex-valued distribution. IEEE Transactions on Fuzzy Systems. 2020; PP (99):1-1.

Chicago/Turabian Style

Fuyuan Xiao. 2020. "On the maximum entropy negation of a complex-valued distribution." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.

Journal article
Published: 30 June 2020 in ISA Transactions
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Multi-sensor data fusion (MSDF) is an efficient technology to enhance the performance of the system with the involvement of different kinds of sensors, which are broadly utilized in many fields at present. However, the data obtained from multi-sensors may have different degrees of uncertainty in the practical applications. Evidence theory is very useful to convey and manage uncertainty without a priori probability, so that it has been proverbially adopted in the information fusion fields. However, in the face of conflicting evidences, it has the possibility of producing counterintuitive results via conducting the Dempster’s combination rule (DCR). To solve the above-mentioned issue, a hybrid MSDF method is exploited through integrating a newly defined evidential credibility measure of evidences based on prospect theory and the evidence theory. More specifically, a series of concepts for the evidential credibility measure are first presented, including the local credibility degree, global credibility degree, evidential credibility estimation and credibility prospect value function to comprehensively describe the award and punish grades in terms of credible evidence and incredible evidence, respectively. Based on the above researches, an appropriate weight for each evidence can be obtained. Ultimately, the weight of each evidence is leveraged to amend the primitive evidences before conducting DCR. The results attained in the experiments demonstrate that the hybrid MSDF approach is efficient and superior to handle conflict evidences as well as the application in data fusion problems.

ACS Style

Fuyuan Xiao. Evidence combination based on prospect theory for multi-sensor data fusion. ISA Transactions 2020, 106, 253 -261.

AMA Style

Fuyuan Xiao. Evidence combination based on prospect theory for multi-sensor data fusion. ISA Transactions. 2020; 106 ():253-261.

Chicago/Turabian Style

Fuyuan Xiao. 2020. "Evidence combination based on prospect theory for multi-sensor data fusion." ISA Transactions 106, no. : 253-261.

Journal article
Published: 15 June 2020 in IEEE Transactions on Fuzzy Systems
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Dempster-Shafer evidence (DSE) theory, which allows combining pieces of evidence from different data sources to derive a degree of belief function that is a type of fuzzy measure, is a general framework for reasoning with uncertainty. In this framework, how to optimally manage the conflicts of multiple pieces of evidence in DSE remains an open issue to support decision making. The existing conflict measurement approaches can achieve acceptable outcomes but do not fully consider the optimization at the decision-making level using the novel measurement of conflicts. In this paper, we proposed a novel evidential correlation coefficient (ECC) for belief functions by measuring the conflict between two pieces of evidence in decision making. Then, we investigated the properties of our proposed evidential correlation and conflict coefficients, which are all proven to satisfy the desirable properties for conflict measurement, including nonnegativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. We also presented several examples and comparisons to demonstrate the superiority of our proposed ECC method. Finally, we applied the proposed ECC in a decision-making application of motor rotor fault diagnosis, which verified the practicability and effectiveness of our proposed novel measurement.

ACS Style

Fuyuan Xiao; Zehong Cao; Alireza Jolfaei. A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis. IEEE Transactions on Fuzzy Systems 2020, 29, 186 -197.

AMA Style

Fuyuan Xiao, Zehong Cao, Alireza Jolfaei. A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis. IEEE Transactions on Fuzzy Systems. 2020; 29 (1):186-197.

Chicago/Turabian Style

Fuyuan Xiao; Zehong Cao; Alireza Jolfaei. 2020. "A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis." IEEE Transactions on Fuzzy Systems 29, no. 1: 186-197.

Article
Published: 16 May 2020 in Applied Intelligence
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Dempster–Shafer evidence theory has been widely used in various fields of applications, because of the flexibility and effectiveness in modeling uncertainties without prior information. However, the existing evidence theory is insufficient to consider the situations where it has no capability to express the fluctuations of data at a given phase of time during their execution, and the uncertainty and imprecision which are inevitably involved in the data occur concurrently with changes to the phase or periodicity of the data. In this paper, therefore, a generalized Dempster–Shafer evidence theory is proposed. To be specific, a mass function in the generalized Dempster–Shafer evidence theory is modeled by a complex number, called as a complex basic belief assignment, which has more powerful ability to express uncertain information. Based on that, a generalized Dempster’s combination rule is exploited. In contrast to the classical Dempster’s combination rule, the condition in terms of the conflict coefficient between the evidences is released in the generalized Dempster’s combination rule. Hence, it is more general and applicable than the classical Dempster’s combination rule. When the complex mass function is degenerated from complex numbers to real numbers, the generalized Dempster’s combination rule degenerates to the classical evidence theory under the condition that the conflict coefficient between the evidences is less than 1. In a word, this generalized Dempster–Shafer evidence theory provides a promising way to model and handle more uncertain information. Thanks to this advantage, an algorithm for decision-making is devised based on the generalized Dempster–Shafer evidence theory. Finally, an application in a medical diagnosis illustrates the efficiency and practicability of the proposed algorithm.

ACS Style

Fuyuan Xiao. Generalization of Dempster–Shafer theory: A complex mass function. Applied Intelligence 2020, 50, 3266 -3275.

AMA Style

Fuyuan Xiao. Generalization of Dempster–Shafer theory: A complex mass function. Applied Intelligence. 2020; 50 (10):3266-3275.

Chicago/Turabian Style

Fuyuan Xiao. 2020. "Generalization of Dempster–Shafer theory: A complex mass function." Applied Intelligence 50, no. 10: 3266-3275.

Foundations
Published: 13 May 2020 in Soft Computing
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The belief interval-valued soft set (BIVSS) combines soft set theory and belief interval value (Dempster–Shafer theory). In this study, we propose a generalized belief interval-valued soft set (GBIVSS) approach and explore the associated properties of this approach in decision-making applications. Using the score function, the scoring function and similarity measure used to compare the relationships between GBIVSS are proposed. Then, we applied the GBIVSS to deal with multi-attribute decision making (MADM) problems. Furthermore, we used a case study of car purchase to illustrate the rationality of the proposed approach. In addition, we compare the effectiveness and advantages of our proposed approach and other existing models, which show superior performance in our proposed approach. GBIVSS provides a solution for multi-attribute problems.

ACS Style

Cuiping Cheng; Zehong Cao; Fuyuan Xiao. A generalized belief interval-valued soft set with applications in decision making. Soft Computing 2020, 24, 9339 -9350.

AMA Style

Cuiping Cheng, Zehong Cao, Fuyuan Xiao. A generalized belief interval-valued soft set with applications in decision making. Soft Computing. 2020; 24 (13):9339-9350.

Chicago/Turabian Style

Cuiping Cheng; Zehong Cao; Fuyuan Xiao. 2020. "A generalized belief interval-valued soft set with applications in decision making." Soft Computing 24, no. 13: 9339-9350.

Article
Published: 21 February 2020 in Applied Intelligence
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The optimization in multi-criteria decision making under uncertain conditions has attracted more and more scholars in recent years. However, it is still an open issue that how to better evaluate the satisfaction with more complex objects. Since the great performance of intuitionistic fuzzy set on handling the uncertain information, in this paper, a new fuzzy linguistic model for non-scalar criteria satisfaction expressed via intuitionistic fuzzy sets is proposed, which makes experts evaluate more objectively. Moreover, a corresponding aggregation approach based on the Choquet probabilistic exceedance method is also proposed. After a series of calculation processes, the final aggregated results embodied by intuitionistic fuzzy sets (IFSs) can be obtained. Then by converting them into the belief intervals, the best alternative can be selected more objectively. In addition, two real-life applications are shown to demonstrate the practicality of proposed method.

ACS Style

Zeyi Liu; Fuyuan Xiao. An intuitionistic linguistic MCDM model based on probabilistic exceedance method and evidence theory. Applied Intelligence 2020, 50, 1979 -1995.

AMA Style

Zeyi Liu, Fuyuan Xiao. An intuitionistic linguistic MCDM model based on probabilistic exceedance method and evidence theory. Applied Intelligence. 2020; 50 (6):1979-1995.

Chicago/Turabian Style

Zeyi Liu; Fuyuan Xiao. 2020. "An intuitionistic linguistic MCDM model based on probabilistic exceedance method and evidence theory." Applied Intelligence 50, no. 6: 1979-1995.

Journal article
Published: 10 February 2020 in IEEE Transactions on Fuzzy Systems
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Forecasting time series is an emerging topic in operational research. Existing time series models have limited prediction accuracy when faced with the characteristics of nonlinearity and nonstationarity in complex situations related to energy and finance. To enhance overall prediction capabilities and improve forecasting accuracy, we propose a fuzzy interval time series forecasting model on the basis of network-based multiple time-frequency spaces and the induced ordered weighted averaging aggregation (IOWA) operation. Specifically, a time series signal is decomposed into ensemble empirical modes and then reconstructed as various time-frequency spaces, which are transformed into visibility graphs. Then, forecasting intervals in different spaces can be collected after the local random walker link prediction model is adopted. Furthermore, a rule-based representation value function inspired by Yager's golden rule approach is defined, and an appropriate representation value is calculated. Finally, after IOWA is used to aggregate the forecasting outcomes in different time-frequency spaces, the final forecast value can be obtained from the fuzzy forecasting interval. Considering that energy issues are of widespread interest in nature and the social economy, two cases, based on a hydrological time series from the Biliuhe River in China and two well-known sets of financial time series data, TAIEX and HSI, are studied to test the performance of the proposed approach in comparison with existing models. Our results show that the proposed approach can achieve better performance than well-developed models.

ACS Style

Gang Liu; Fuyuan Xiao; Chin-Teng Lin; Zehong Cao. A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation. IEEE Transactions on Fuzzy Systems 2020, 28, 2677 -2690.

AMA Style

Gang Liu, Fuyuan Xiao, Chin-Teng Lin, Zehong Cao. A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation. IEEE Transactions on Fuzzy Systems. 2020; 28 (11):2677-2690.

Chicago/Turabian Style

Gang Liu; Fuyuan Xiao; Chin-Teng Lin; Zehong Cao. 2020. "A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation." IEEE Transactions on Fuzzy Systems 28, no. 11: 2677-2690.

Preprint content
Published: 04 January 2020
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ACS Style

Fuyuan Xiao. A correlation coefficient for complex mass function in evidence theory. 2020, 1 .

AMA Style

Fuyuan Xiao. A correlation coefficient for complex mass function in evidence theory. . 2020; ():1.

Chicago/Turabian Style

Fuyuan Xiao. 2020. "A correlation coefficient for complex mass function in evidence theory." , no. : 1.

Preprint content
Published: 04 January 2020
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The data used in this paper are given in the paper.

ACS Style

Fuyuan Xiao. A correlation coefficient for complex mass function in evidence theory. 2020, 1 .

AMA Style

Fuyuan Xiao. A correlation coefficient for complex mass function in evidence theory. . 2020; ():1.

Chicago/Turabian Style

Fuyuan Xiao. 2020. "A correlation coefficient for complex mass function in evidence theory." , no. : 1.

Research article
Published: 19 November 2019 in Journal of Sensors
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D-S evidence theory is widely used in data fusion. However, the result of Dempster’s combination rule is not efficient and in highly conflicting situation. Though the existing methods have been proved efficient to deal with conflict in some applications, the indirect conflict among evidence is neglected to some degree. To solve this problem, a new method is proposed based on decision-making trial and evaluation laboratory (DEMATEL) and the belief correlation coefficient in this paper. The application in target recognition illustrates the efficiency of the proposed method. Compared with Dempster’s rule, averaging method and weighted averaging method, etc., the results obtained by the proposed method have better performance. The main reason is that the indirect conflict is well addressed in the proposed method.

ACS Style

Wentao Fan; Fuyuan Xiao. A New Conflict Management in Evidence Theory Based on DEMATEL Method. Journal of Sensors 2019, 2019, 1 -12.

AMA Style

Wentao Fan, Fuyuan Xiao. A New Conflict Management in Evidence Theory Based on DEMATEL Method. Journal of Sensors. 2019; 2019 ():1-12.

Chicago/Turabian Style

Wentao Fan; Fuyuan Xiao. 2019. "A New Conflict Management in Evidence Theory Based on DEMATEL Method." Journal of Sensors 2019, no. : 1-12.