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Prof. Patricia Melin
Tijuana Institute of Technology

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Research Keywords & Expertise

0 Evolutionary Algorithms
0 Fuzzy Logic
0 Neural Networks
0 neural network supervised learning
0 Fuzzy Approach

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Fuzzy Logic
Neural Networks
Fuzzy Approach
Evolutionary Algorithms

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Short Biography

Patricia Melin is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico, since 1998. In addition, she serves as the Director of Graduate Studies in Computer Science and is head of the research group on Hybrid Neural Intelligent Systems (2000-present). She holds a Doctorate in Science degree (Doctor Habilitatus D.Sc.) in Computer Science from the Polish Academy of Sciences. Prof. Melin has published nearly 800 publications in indexed journals, book chapters, and conference proceedings, as well as nearly 50 books, and as a result, has achieved more than 18,000 citations, with an H index of 74 in Google Scholar. In addition, she has been awarded the Highly Cited Researcher recognition in the area of Computer Science in 2017 and 2018 by Clarivate Analytics because she is in the top 1% cited author in this area. She has also been the advisor of more than 80 graduate students in computer science at the Ph.D. and Master's degree levels. She is a past President of NAFIPS (North American Fuzzy Information Processing Society) 2019-2020. Prof. Melin is the founding Chair of the Mexican chapter of the IEEE Computational Intelligence Society. She is a member of the IEEE Neural Network Technical Committee (2007 to present), the IEEE Fuzzy System Technical Committee (2014 to present), Chair of the Task Force on Hybrid Intelligent Systems (2007 to present), and she is currently Associate Editor of the Information Sciences Journal.

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Journal article
Published: 19 August 2021 in Axioms
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This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. Previously, we have worked with both kinds of fuzzy systems in different types of benchmark problems and it has been found that the use of fuzzy logic in combination with the differential evolution algorithm gives good results. In some of the studies, it is clearly shown that, when compared to other algorithms, our methodology turns out to be statistically better. In this case, the mutation parameter is dynamically moved during the evolution process by using shadowed and general type-2 fuzzy systems. The main contribution of this work is the ability to determine, through experimentation in a benchmark control problem, which of the two kinds of the used fuzzy systems has better results when combined with the differential evolution algorithm. This is because there are no similar works to our proposal in which shadowed and general type 2 fuzzy systems are used and compared. Moreover, to validate the performance of both fuzzy systems, a noise level is used in the controller, which simulates the disturbances that may exist in the real world and is thus able to validate statistically if there are significant differences between shadowed and general type 2 fuzzy systems.

ACS Style

Patricia Ochoa; Oscar Castillo; Patricia Melin; José Soria. Differential Evolution with Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers. Axioms 2021, 10, 194 .

AMA Style

Patricia Ochoa, Oscar Castillo, Patricia Melin, José Soria. Differential Evolution with Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers. Axioms. 2021; 10 (3):194.

Chicago/Turabian Style

Patricia Ochoa; Oscar Castillo; Patricia Melin; José Soria. 2021. "Differential Evolution with Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers." Axioms 10, no. 3: 194.

Journal article
Published: 24 July 2021 in Sustainability
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In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

ACS Style

Patricia Melin; Oscar Castillo. Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach. Sustainability 2021, 13, 8295 .

AMA Style

Patricia Melin, Oscar Castillo. Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach. Sustainability. 2021; 13 (15):8295.

Chicago/Turabian Style

Patricia Melin; Oscar Castillo. 2021. "Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach." Sustainability 13, no. 15: 8295.

Preprint content
Published: 16 July 2021
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Fuzzy dynamic parameter adaptation has proven to be of great help when it is implemented in bio-inspired algorithms for optimization in different application areas, such as control, mathematical functions, classification, among others. One of the main contributions of this work is the proposed improvement of the Bird Swarm algorithm using a Fuzzy System approach, and we called this improvement the Fuzzy Bird Swarm Algorithm. Furthermore, we use a set of complex Benchmark Functions of the Congress on Evolutionary Computation Competition 2017 to compare the results between the original algorithm and the proposed improvement of the algorithm. The fuzzy system is utilized for the dynamic parameter adaptation of C1 and C2 parameters of the Bird Swarm Algorithm. As a result, the Fuzzy Bird Swarm Algorithm has enhanced exploration and exploitation abilities that help in achieving better results than the Bird Swarm Algorithm. We additionally test the algorithm's performance in a real problem in the medical area, using the optimization of a neural network to obtain the risk of developing hypertension. This neural network uses patient information, such as age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes and if the patient has parents with hypertension. Hypertension is one of the leading causes of heart problems, which in turn are also the top causes of death. Moreover, these days it causes more complications and deaths in people infected with COVID-19, the virus of the ongoing pandemic. Based on the results obtained through the 30 experiments carried out in three different study cases, and the results obtained from the statistical tests, it can be concluded that the proposed method provides better performance when compared with the original method.

ACS Style

Patricia Melin; Ivette Miramontes; Oscar Carvajal; German Prado-Arechiga. Fuzzy Dynamic Parameter Adaptation in the Bird Swarm Algorithm for Neural Network Optimization. 2021, 1 .

AMA Style

Patricia Melin, Ivette Miramontes, Oscar Carvajal, German Prado-Arechiga. Fuzzy Dynamic Parameter Adaptation in the Bird Swarm Algorithm for Neural Network Optimization. . 2021; ():1.

Chicago/Turabian Style

Patricia Melin; Ivette Miramontes; Oscar Carvajal; German Prado-Arechiga. 2021. "Fuzzy Dynamic Parameter Adaptation in the Bird Swarm Algorithm for Neural Network Optimization." , no. : 1.

Journal article
Published: 10 July 2021 in Chaos, Solitons & Fractals
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This article is presenting a first attempt on a proposed fuzzy fractal control method for efficiently controlling nonlinear dynamic systems. The main goal is to combine the main advantages of fractal theoretical concepts and fuzzy logic theory for achieving efficient control of nonlinear dynamic systems. The concept coming from Fractal theory, known as the fractal dimension, can be utilized to measure the complexity of the dynamic behavior of a non-linear plant. On the other hand, fuzzy logic theory can be used to represent and capture the expert knowledge in controlling a plant. In addition, fuzzy logic enables to manage the uncertainty involved in the decision-making process for achieving efficient control of a non-linear plant. We illustrate the proposed fuzzy fractal control method with the current worldwide situation that requires achieving an efficient control of the COVID-19 pandemics.

ACS Style

Oscar Castillo; Patricia Melin. A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the COVID-19 pandemics. Chaos, Solitons & Fractals 2021, 151, 111250 .

AMA Style

Oscar Castillo, Patricia Melin. A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the COVID-19 pandemics. Chaos, Solitons & Fractals. 2021; 151 ():111250.

Chicago/Turabian Style

Oscar Castillo; Patricia Melin. 2021. "A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the COVID-19 pandemics." Chaos, Solitons & Fractals 151, no. : 111250.

Preprint
Published: 25 June 2021
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In this article, the evolution in space and in time of the coronavirus pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries. Self-organizing neural networks possess the capability for clustering countries in the space domain based on their similar characteristics with respect to their coronavirus cases. In this form enabling finding the countries that are having similar behavior and thus can benefit from utilizing the same methods in fighting the virus propagation. To validate the approach, publicly available datasets of coronavirus cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of time series of the countries. Then, a hybrid combination of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient COVID-19 forecasting of the countries. Relevant conclusions have emerged from this study, that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. A lot of the existing works concerned with the Coronavirus have look at the problem mostly from the temporal viewpoint that is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant to improve the forecasting ability. The most relevant contribution of this article is the proposal of combining neural networks with a self-organizing nature for clustering countries with high similarity and the fuzzy fractal approach for being able to forecast the times series and help in planning control actions for the Coronavirus pandemic.

ACS Style

Patricia Melin; Oscar Castillo. Spatial and Temporal Spread of the Coronavirus Pandemic using Self Organizing Neural Networks and a Fuzzy Fractal Approach. 2021, 1 .

AMA Style

Patricia Melin, Oscar Castillo. Spatial and Temporal Spread of the Coronavirus Pandemic using Self Organizing Neural Networks and a Fuzzy Fractal Approach. . 2021; ():1.

Chicago/Turabian Style

Patricia Melin; Oscar Castillo. 2021. "Spatial and Temporal Spread of the Coronavirus Pandemic using Self Organizing Neural Networks and a Fuzzy Fractal Approach." , no. : 1.

Preprint
Published: 15 June 2021
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This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. In this case, the mutation parameter is dynamically moved during the evolution process by using a shadowed and general type-2 fuzzy systems. The main idea of this work is to make a performance comparison between using shadowed and general type 2 fuzzy systems as controllers of the mutation parameter in differential evolution. The performance is compared with the problem of optimizing fuzzy controllers for a D.C. Motor. Simulation results show that general type-2 fuzzy systems are better when higher levels of noise are considered in the controller.

ACS Style

Patricia Ochoa; Oscar Castillo; Patricia Melin; José Soria. Differential Evolution With Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers. 2021, 1 .

AMA Style

Patricia Ochoa, Oscar Castillo, Patricia Melin, José Soria. Differential Evolution With Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers. . 2021; ():1.

Chicago/Turabian Style

Patricia Ochoa; Oscar Castillo; Patricia Melin; José Soria. 2021. "Differential Evolution With Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers." , no. : 1.

Journal article
Published: 11 June 2021 in Expert Systems with Applications
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One of the most studied application areas of intelligent systems is the classification area, and this is because classification covers a wide range of real-world problems. Some examples are fault-diagnosis, image segmentation, medical diagnosis, among others. In most cases, the intelligent systems designed for the solution of this kind of problems are based on supervised learning, which is based on learning how to classify with previous datasets for finding relations between the inputs and outputs. The main focus of the present paper is the supervised generation of general type-2 fuzzy classifiers with a new strategy for modeling data uncertainty. The proposed methodology includes a mix of concepts, such as the use of embedded type-1 membership functions, statistical concepts such as the quartiles, and nature inspired optimization methods. The classifiers generated with the proposed methodology are compared with respect to other general type-2 fuzzy classifiers based on symmetric uncertainty to evaluate their performance, in this way obtaining interesting results for medical diagnosis with benchmark data sets.

ACS Style

Emanuel Ontiveros-Robles; Oscar Castillo; Patricia Melin. Towards asymmetric uncertainty modeling in designing General Type-2 Fuzzy classifiers for medical diagnosis. Expert Systems with Applications 2021, 183, 115370 .

AMA Style

Emanuel Ontiveros-Robles, Oscar Castillo, Patricia Melin. Towards asymmetric uncertainty modeling in designing General Type-2 Fuzzy classifiers for medical diagnosis. Expert Systems with Applications. 2021; 183 ():115370.

Chicago/Turabian Style

Emanuel Ontiveros-Robles; Oscar Castillo; Patricia Melin. 2021. "Towards asymmetric uncertainty modeling in designing General Type-2 Fuzzy classifiers for medical diagnosis." Expert Systems with Applications 183, no. : 115370.

Chapter
Published: 04 June 2021 in Self-Powered and Soft Polymer MEMS/NEMS Devices
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The present chapter presents the proposed methodology for the design of General Type-2 Fuzzy Inference Systems for diagnosis problems.

ACS Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. Proposed Methodology. Self-Powered and Soft Polymer MEMS/NEMS Devices 2021, 29 -62.

AMA Style

Patricia Melin, Emanuel Ontiveros-Robles, Oscar Castillo. Proposed Methodology. Self-Powered and Soft Polymer MEMS/NEMS Devices. 2021; ():29-62.

Chicago/Turabian Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. 2021. "Proposed Methodology." Self-Powered and Soft Polymer MEMS/NEMS Devices , no. : 29-62.

Chapter
Published: 04 June 2021 in Self-Powered and Soft Polymer MEMS/NEMS Devices
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In this chapter the results obtained for the previously presented approaches are presented, in order to compare the developed systems based on the performance measured with the accuracy.

ACS Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. Experimental Results. Self-Powered and Soft Polymer MEMS/NEMS Devices 2021, 63 -72.

AMA Style

Patricia Melin, Emanuel Ontiveros-Robles, Oscar Castillo. Experimental Results. Self-Powered and Soft Polymer MEMS/NEMS Devices. 2021; ():63-72.

Chicago/Turabian Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. 2021. "Experimental Results." Self-Powered and Soft Polymer MEMS/NEMS Devices , no. : 63-72.

Chapter
Published: 04 June 2021 in Self-Powered and Soft Polymer MEMS/NEMS Devices
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In this chapter the theoretical elements necessary to the development of the proposed approach and methodologies are introduced.

ACS Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. Background and Theory. Self-Powered and Soft Polymer MEMS/NEMS Devices 2021, 5 -28.

AMA Style

Patricia Melin, Emanuel Ontiveros-Robles, Oscar Castillo. Background and Theory. Self-Powered and Soft Polymer MEMS/NEMS Devices. 2021; ():5-28.

Chicago/Turabian Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. 2021. "Background and Theory." Self-Powered and Soft Polymer MEMS/NEMS Devices , no. : 5-28.

Chapter
Published: 04 June 2021 in Self-Powered and Soft Polymer MEMS/NEMS Devices
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Nowadays, one of the most important applications of intelligent systems are expert systems for decision making, these kinds of systems can provide experts with a tool for expert systems.

ACS Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. Introduction. Self-Powered and Soft Polymer MEMS/NEMS Devices 2021, 1 -3.

AMA Style

Patricia Melin, Emanuel Ontiveros-Robles, Oscar Castillo. Introduction. Self-Powered and Soft Polymer MEMS/NEMS Devices. 2021; ():1-3.

Chicago/Turabian Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. 2021. "Introduction." Self-Powered and Soft Polymer MEMS/NEMS Devices , no. : 1-3.

Chapter
Published: 04 June 2021 in Self-Powered and Soft Polymer MEMS/NEMS Devices
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As can be observed in the experimental results, some specific T2 FDS proposed with our methodology

ACS Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. Discussion of Results. Self-Powered and Soft Polymer MEMS/NEMS Devices 2021, 73 -75.

AMA Style

Patricia Melin, Emanuel Ontiveros-Robles, Oscar Castillo. Discussion of Results. Self-Powered and Soft Polymer MEMS/NEMS Devices. 2021; ():73-75.

Chicago/Turabian Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. 2021. "Discussion of Results." Self-Powered and Soft Polymer MEMS/NEMS Devices , no. : 73-75.

Chapter
Published: 04 June 2021 in Self-Powered and Soft Polymer MEMS/NEMS Devices
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In the present work, it was proposed a methodology for the automatic design of Fuzzy.

ACS Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. Conclusions. Self-Powered and Soft Polymer MEMS/NEMS Devices 2021, 77 -78.

AMA Style

Patricia Melin, Emanuel Ontiveros-Robles, Oscar Castillo. Conclusions. Self-Powered and Soft Polymer MEMS/NEMS Devices. 2021; ():77-78.

Chicago/Turabian Style

Patricia Melin; Emanuel Ontiveros-Robles; Oscar Castillo. 2021. "Conclusions." Self-Powered and Soft Polymer MEMS/NEMS Devices , no. : 77-78.

Conference paper
Published: 17 April 2021 in Advances in Intelligent Systems and Computing
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In this work a real-coded genetic algorithm for parameter optimization of the membership functions of the inputs and the outputs of a fuzzy inference system applied to diabetes classification is proposed. The main goal of this article is to show the advantages that parameter optimization of all the inputs and output using a real-coded genetic algorithm. The dataset used in this work to validate the approach is the PIMA Indian Diabetes dataset, where we have selected five attributes to perform the parameter optimization. Being the Diabetes a disease that has been affecting many lives in the world, for this reason, this work seeks to find a better classification.

ACS Style

Julio C. Monica; Patricia Melin; Daniela Sanchez. Optimal Design of a Fuzzy System with a Real-Coded Genetic Algorithm for Diabetes Classification. Advances in Intelligent Systems and Computing 2021, 320 -329.

AMA Style

Julio C. Monica, Patricia Melin, Daniela Sanchez. Optimal Design of a Fuzzy System with a Real-Coded Genetic Algorithm for Diabetes Classification. Advances in Intelligent Systems and Computing. 2021; ():320-329.

Chicago/Turabian Style

Julio C. Monica; Patricia Melin; Daniela Sanchez. 2021. "Optimal Design of a Fuzzy System with a Real-Coded Genetic Algorithm for Diabetes Classification." Advances in Intelligent Systems and Computing , no. : 320-329.

Journal article
Published: 17 April 2021 in Fractal and Fractional
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Stochastic fractal search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic stochastic diffusion property, an improved dynamic stochastic fractal search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid, and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

ACS Style

Marylu Lagunes; Oscar Castillo; Fevrier Valdez; Jose Soria; Patricia Melin. A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation. Fractal and Fractional 2021, 5, 33 .

AMA Style

Marylu Lagunes, Oscar Castillo, Fevrier Valdez, Jose Soria, Patricia Melin. A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation. Fractal and Fractional. 2021; 5 (2):33.

Chicago/Turabian Style

Marylu Lagunes; Oscar Castillo; Fevrier Valdez; Jose Soria; Patricia Melin. 2021. "A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation." Fractal and Fractional 5, no. 2: 33.

Journal article
Published: 12 April 2021 in Algorithms
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In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones, nature-inspired algorithms that have been published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new area of nature and bio-inspired optimization of fuzzy clustering.

ACS Style

Fevrier Valdez; Oscar Castillo; Patricia Melin. Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering. Algorithms 2021, 14, 122 .

AMA Style

Fevrier Valdez, Oscar Castillo, Patricia Melin. Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering. Algorithms. 2021; 14 (4):122.

Chicago/Turabian Style

Fevrier Valdez; Oscar Castillo; Patricia Melin. 2021. "Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering." Algorithms 14, no. 4: 122.

Preprint
Published: 26 March 2021
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Metaheuristic algorithms are widely used as optimization methods, due to their global exploration and exploitation characteristics, which obtain better results than a simple heuristic. The Stochastic Fractal Search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic-stochastic diffusion property, an improved Dynamic Stochastic Fractal Search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

ACS Style

Marylu Lagunes; Oscar Castillo; Fevrier Valdez; José Soria; Patricia Melin. A new Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation. 2021, 1 .

AMA Style

Marylu Lagunes, Oscar Castillo, Fevrier Valdez, José Soria, Patricia Melin. A new Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation. . 2021; ():1.

Chicago/Turabian Style

Marylu Lagunes; Oscar Castillo; Fevrier Valdez; José Soria; Patricia Melin. 2021. "A new Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation." , no. : 1.

Chapter
Published: 25 March 2021 in Econometrics for Financial Applications
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In this paper, we propose to search for the best number of filters in the convolution layer of a convolutional neural network, we used a fuzzy logic system to find the most suitable parameters for the proposed case study. In addition to this we make use of the Fuzzy Gravitational Search Algorithm method to find the parameters of the fuzzy system memberships.

ACS Style

Yutzil Poma; Patricia Melin. Estimation of the Number of Filters in the Convolution Layers of a Convolutional Neural Network Using a Fuzzy Logic System. Econometrics for Financial Applications 2021, 1 -14.

AMA Style

Yutzil Poma, Patricia Melin. Estimation of the Number of Filters in the Convolution Layers of a Convolutional Neural Network Using a Fuzzy Logic System. Econometrics for Financial Applications. 2021; ():1-14.

Chicago/Turabian Style

Yutzil Poma; Patricia Melin. 2021. "Estimation of the Number of Filters in the Convolution Layers of a Convolutional Neural Network Using a Fuzzy Logic System." Econometrics for Financial Applications , no. : 1-14.

Chapter
Published: 25 March 2021 in Econometrics for Financial Applications
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In this paper the development of the general architecture of a method to design recurrent ensemble neural networks for time series prediction is presented. Therefore, experiments are shown for the ensemble recurrent neural network, as well as the integration of the responses of the ensemble recurrent neural network, with integration by average, weighted average integration, type-1, type-2 and Generalized Type-2 fuzzy systems. The time series used to test the proposed architecture is that of petroleum. The simulation results show the effectiveness of the proposed method.

ACS Style

Martha Pulido; Patricia Melin. Ensemble Recurrent Neural Networks for Complex Time Series Prediction with Integration Methods. Econometrics for Financial Applications 2021, 71 -83.

AMA Style

Martha Pulido, Patricia Melin. Ensemble Recurrent Neural Networks for Complex Time Series Prediction with Integration Methods. Econometrics for Financial Applications. 2021; ():71-83.

Chicago/Turabian Style

Martha Pulido; Patricia Melin. 2021. "Ensemble Recurrent Neural Networks for Complex Time Series Prediction with Integration Methods." Econometrics for Financial Applications , no. : 71-83.

Chapter
Published: 25 March 2021 in Econometrics for Financial Applications
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These days, with the current situation we are experiencing worldwide due to the pandemic, it is of utmost importance to know our state of health, speaking more specifically of our cardiovascular health. Soft computing can be used by medical experts as a powerful tool to help and facilitate providing a diagnosis of our state of health. The objective of this work is to create a modular neural network to obtain the risk diagnosis that a patient has in developing a cardiovascular event in a period of 10 years likewise, find the heart age. In order to provide this information, a series of risk factors will be given as input to each of the modules, such as age, gender, body mass index, systolic pressure, if the patient is diabetic, if the patient smokes, if he/she is under hypertension treatment. Each module is optimized with two bio-inspired algorithms to test its performance and thereby obtain the best results to provide an accurate diagnosis.

ACS Style

Ivette Miramontes; Patricia Melin; Oscar Carvajal; German Prado-Arechiga. Optimization of Modular Neural Networks for the Diagnosis of Cardiovascular Risk. Econometrics for Financial Applications 2021, 99 -111.

AMA Style

Ivette Miramontes, Patricia Melin, Oscar Carvajal, German Prado-Arechiga. Optimization of Modular Neural Networks for the Diagnosis of Cardiovascular Risk. Econometrics for Financial Applications. 2021; ():99-111.

Chicago/Turabian Style

Ivette Miramontes; Patricia Melin; Oscar Carvajal; German Prado-Arechiga. 2021. "Optimization of Modular Neural Networks for the Diagnosis of Cardiovascular Risk." Econometrics for Financial Applications , no. : 99-111.