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This paper designed a 7-DOF redundant robot manipulator that can flexibly and efficiently pick-up random objects. The developed 7-DOF machine with an additional redundancy achieved great progress in terms of flexibility and efficiency in the operational space. A robot operating system (ROS) was used to configure the manipulator system’s software modules, supporting convenient system interface, appropriate movement control policy, and powerful hardware device management for better regulation of the manipulator’s motions. A 3D type Point Cloud Library (PCL) was utilized to perform a novel point cloud image pre-processing method that did not only reduce the point cloud number but also maintained the original quality. The results of the experiment showed that the estimation speed in object detection and recognition procedure improved significantly. The redundant robot manipulator architecture with the two-stage search algorithm was able to find the optimal null space. Suitable parameters in D-H transformation of forward kinematics were selected to efficiently control and position the manipulator in the right posture. Meanwhile, the reverse kinematics estimated all angles of the joints through the known manipulator position, orientation, and redundancy. Finally, motion panning implementation of manipulator rapidly and successfully reached the random object position and automatically drew it up to approximate the desired target.
Ching-Chang Wong; Hsuan Ming Feng; Yu-Cheng Lai; Hsiang-Yun Chen. Manipulator system designs for drawing random objects through point cloud posture estimation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2021, 1 .
AMA StyleChing-Chang Wong, Hsuan Ming Feng, Yu-Cheng Lai, Hsiang-Yun Chen. Manipulator system designs for drawing random objects through point cloud posture estimation. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 2021; ():1.
Chicago/Turabian StyleChing-Chang Wong; Hsuan Ming Feng; Yu-Cheng Lai; Hsiang-Yun Chen. 2021. "Manipulator system designs for drawing random objects through point cloud posture estimation." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture , no. : 1.
Ching-Chang Wong; Hsuan-Ming Feng; Yu-Cheng Lai; Chia-Jun Yu. Ant Colony Optimization and image model-based robot manipulator system for pick-and-place tasks. Journal of Intelligent & Fuzzy Systems 2019, 36, 1083 -1098.
AMA StyleChing-Chang Wong, Hsuan-Ming Feng, Yu-Cheng Lai, Chia-Jun Yu. Ant Colony Optimization and image model-based robot manipulator system for pick-and-place tasks. Journal of Intelligent & Fuzzy Systems. 2019; 36 (2):1083-1098.
Chicago/Turabian StyleChing-Chang Wong; Hsuan-Ming Feng; Yu-Cheng Lai; Chia-Jun Yu. 2019. "Ant Colony Optimization and image model-based robot manipulator system for pick-and-place tasks." Journal of Intelligent & Fuzzy Systems 36, no. 2: 1083-1098.
Ching-Chang Wong; Hua-Ching Chen; Chin-Tan Lee; Chien-Chung Wang; Hsuan-Ming. Feng. High interactive sensory robot system design in the indoor autonomous services applications. Journal of Intelligent & Fuzzy Systems 2019, 36, 1259 -1271.
AMA StyleChing-Chang Wong, Hua-Ching Chen, Chin-Tan Lee, Chien-Chung Wang, Hsuan-Ming. Feng. High interactive sensory robot system design in the indoor autonomous services applications. Journal of Intelligent & Fuzzy Systems. 2019; 36 (2):1259-1271.
Chicago/Turabian StyleChing-Chang Wong; Hua-Ching Chen; Chin-Tan Lee; Chien-Chung Wang; Hsuan-Ming. Feng. 2019. "High interactive sensory robot system design in the indoor autonomous services applications." Journal of Intelligent & Fuzzy Systems 36, no. 2: 1259-1271.
Detian Huang Huang; Peiting Gu; Hsuan-Ming Feng; Yanming Lin; Lixin Zheng. Robust Visual Tracking Model Designs Through Kernelized Correlation Filters. Intelligent Automation & Soft Computing 2019, 1 -1.
AMA StyleDetian Huang Huang, Peiting Gu, Hsuan-Ming Feng, Yanming Lin, Lixin Zheng. Robust Visual Tracking Model Designs Through Kernelized Correlation Filters. Intelligent Automation & Soft Computing. 2019; ():1-1.
Chicago/Turabian StyleDetian Huang Huang; Peiting Gu; Hsuan-Ming Feng; Yanming Lin; Lixin Zheng. 2019. "Robust Visual Tracking Model Designs Through Kernelized Correlation Filters." Intelligent Automation & Soft Computing , no. : 1-1.
Peizhong Liu; Xiaofang Liu; Yanming Luo; Yongzhao Du; Yulin Fan; Hsuan-Ming Feng. An Enhanced Exploitation Artificial Bee Colony Algorithm in Automatic Functional Approximations. Intelligent Automation & Soft Computing 2019, 1 -1.
AMA StylePeizhong Liu, Xiaofang Liu, Yanming Luo, Yongzhao Du, Yulin Fan, Hsuan-Ming Feng. An Enhanced Exploitation Artificial Bee Colony Algorithm in Automatic Functional Approximations. Intelligent Automation & Soft Computing. 2019; ():1-1.
Chicago/Turabian StylePeizhong Liu; Xiaofang Liu; Yanming Luo; Yongzhao Du; Yulin Fan; Hsuan-Ming Feng. 2019. "An Enhanced Exploitation Artificial Bee Colony Algorithm in Automatic Functional Approximations." Intelligent Automation & Soft Computing , no. : 1-1.
This paper proposed an image preparing technology to remove completely the noise effects of unexpected conditions and actually extract primary feature of human pose in indoor environment. Color image conversation with YCbCr, enhanced image intelligibility with a median filter, one OPEN operation and one labeling algorithm, are applied to detect the object`s boundary. A dynamic image segmentation matching algorithm generated human feature vectors, which are used to search the most similar pattern for approximating the correct human pose. Several experiments show that the proposed dynamic image segmentation matching algorithm actually detected the real characteristics of human pose and makes the great support to re-cover the various image feature problem in complex environment. Future work for an intelligent human pose is used to virtually control home equipment in everywhere.
Hsuan-Ming Feng; Hua-Ching Chen; Ching-Chang Wong. Human Image Recognition through Dynamic Mapping Generation. 2018 4th International Conference on Green Technology and Sustainable Development (GTSD) 2018, 749 -752.
AMA StyleHsuan-Ming Feng, Hua-Ching Chen, Ching-Chang Wong. Human Image Recognition through Dynamic Mapping Generation. 2018 4th International Conference on Green Technology and Sustainable Development (GTSD). 2018; ():749-752.
Chicago/Turabian StyleHsuan-Ming Feng; Hua-Ching Chen; Ching-Chang Wong. 2018. "Human Image Recognition through Dynamic Mapping Generation." 2018 4th International Conference on Green Technology and Sustainable Development (GTSD) , no. : 749-752.
This study utilized the Robot Operating System (ROS) to simulate various poses of humanoid robot. The distributed ROS architectures efficiently communicate with each other point to make sure the correct sequential procedures and execute full information exchange through the peer to peer topology. Humanoid robot software designed in Linux-based ROS environment to integrate the software and hardware system plant through ROS. The advanced Gazebo presents the cyber-physical behavior of humanoid robot in the reality experiments.
Hsuan-Ming Feng; Ching-Chang Wong; Chih-Cheng Liu; Sheng-Ru Xiao. ROS-Based Humanoid Robot Pose Control System Design. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2018, 4089 -4093.
AMA StyleHsuan-Ming Feng, Ching-Chang Wong, Chih-Cheng Liu, Sheng-Ru Xiao. ROS-Based Humanoid Robot Pose Control System Design. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2018; ():4089-4093.
Chicago/Turabian StyleHsuan-Ming Feng; Ching-Chang Wong; Chih-Cheng Liu; Sheng-Ru Xiao. 2018. "ROS-Based Humanoid Robot Pose Control System Design." 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , no. : 4089-4093.
Mangroves are valuable contributors to coastal ecosystems, and remote sensing is an indispensable way to obtain knowledge of the dynamics of mangrove ecosystems. Due to the similar spectral features between mangroves and other land cover types, challenges are posed since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this paper, we propose a classification method named the multi-feature joint sparse algorithm (MF-SRU), in which spectral, topographic, and textural features are integrated as the decision-making features, and sparse representation of both center pixels and their eight neighborhood pixels is proposed to represent the spatial correlation of neighboring pixels, which can make good use of the spatial correlation of adjacent pixels. Experiments are performed on Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in Southeastern China, and the results show that the proposed method can effectively improve the extraction accuracy of mangroves.
Yan-Min Luo; Yi Ouyang; Ren-Cheng Zhang; Hsuan-Ming Feng. Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images. ISPRS International Journal of Geo-Information 2017, 6, 177 .
AMA StyleYan-Min Luo, Yi Ouyang, Ren-Cheng Zhang, Hsuan-Ming Feng. Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images. ISPRS International Journal of Geo-Information. 2017; 6 (6):177.
Chicago/Turabian StyleYan-Min Luo; Yi Ouyang; Ren-Cheng Zhang; Hsuan-Ming Feng. 2017. "Multi-Feature Joint Sparse Model for the Classification of Mangrove Remote Sensing Images." ISPRS International Journal of Geo-Information 6, no. 6: 177.
This study developed a fuzzy image model system for transmitting data over a wireless network channel to efficiently realize human activity in virtual images presentation. Because of the excellent mobile characteristics of wireless sensing networks, small devices are very desirable for local-area deployment. Complex model identification problems, such as acquiring and handling wireless image patterns, require analyzing a large amount of data, which occupies a long time at an acceptable transmission quality. In the proposed system, a cross-layer access method is employed to improve the visual clarity. Image packets are assigned to tune the category queue priority, with the probability allocated through a Markov chain model. This is a favorable approach to balancing the wireless image transmission traffic load. The similarity mixing algorithm, which is based on the maximal similarity and minimal disparity concepts, is used to aggregate the primary image features. The collected image patterns with converted coding vectors are efficiently trained through a human feature recognition procedure to generate a human model. A human action is received in real time from wireless sensing networks, and the image feature is retrieved by approximating a higher compatibility in practice simulations. The fuzzy image model uses the simple region-based evaluation and flexible extraction concepts to describe appropriate image partitions. This technology provides the highest possibility of human feature maps to identify the current action and offers a simple method for detecting human activity in indoor environments. Several human sensing and feature mapping experiments were conducted to verify the feasibility of applying the image recognition technology in nonlinear, time variant, and uncertain human activity problems. This study integrates numerous advantages from the mobility of wireless sensing; the proposed system efficiently controls congested image packages and easily confirms their related human activity. Experimental results verify that 60 testing frames approach about 96.6% accuracy within 3 s. These evaluations illustrate that it is applicable usage in some indoor environments.
Hua-Ching Chen; Ching-Chang Wong; Hsuan-Ming Feng. Wireless image fuzzy recognition system for human activity. Multimedia Tools and Applications 2017, 76, 25231 -25251.
AMA StyleHua-Ching Chen, Ching-Chang Wong, Hsuan-Ming Feng. Wireless image fuzzy recognition system for human activity. Multimedia Tools and Applications. 2017; 76 (23):25231-25251.
Chicago/Turabian StyleHua-Ching Chen; Ching-Chang Wong; Hsuan-Ming Feng. 2017. "Wireless image fuzzy recognition system for human activity." Multimedia Tools and Applications 76, no. 23: 25231-25251.
This paper investigates the Carrier Current Line Systems (CCLS) technologies of Machine to Machine (M2M) architecture which applied for mobile station coverage working with metro, high speed railway, and subway such as analysis for public transport of an indoor transition system. It is based on the theory and practical engineering principle which provide guidelines and formulas for link budget design to help designers fully control and analyze the single output power of uplink and downlink between Fiber Repeaters (FR) and mobile station as well as base station. Finally, the results of this leakage cable system are successfully applied to indoor coverage design for metro rapid transit system which are easily installed cellular over fiber solutions for WCDMA/LTE access is becoming Ubiquitous Network to Internet of Thing (IOT) real case hierarchy of telecommunication.
Hua-Ching Chen; Chia-Lun Wu; Jwo-Shiun Sun; Hsuan-Ming Feng. Carrier Current Line Systems Technologies in M2M Architecture for Wireless Communication. Journal of Sensors 2016, 2016, 1 -10.
AMA StyleHua-Ching Chen, Chia-Lun Wu, Jwo-Shiun Sun, Hsuan-Ming Feng. Carrier Current Line Systems Technologies in M2M Architecture for Wireless Communication. Journal of Sensors. 2016; 2016 ():1-10.
Chicago/Turabian StyleHua-Ching Chen; Chia-Lun Wu; Jwo-Shiun Sun; Hsuan-Ming Feng. 2016. "Carrier Current Line Systems Technologies in M2M Architecture for Wireless Communication." Journal of Sensors 2016, no. : 1-10.
The novel random forests algorithm with variables random input and random combination (Forest_RI_RC) machine was proposed to improve the weakness of low accuracy and over-fitting phenomenon in single decision tree. The proposed method produces more and more selections and combinations to increase the possibility of the best decision-making features. This way reduces the correlation coefficient of the random forests, which efficiently lead to the lower generalization error and approach the higher classification accuracy. The standard machine learning datasets were used to verify the validity of the classification. The simulation results showed that the novel algorithm with the multiple classifiers to concurrently segment the objects and achieve the smaller generalization error. Finally, the algorithm was applied to the classified problems of mangrove remote sensing image. Software simulations presents that the classification accuracy is basically stable at around 90 %. This performance is better than the other two decision tree and bagging methods.
Yan-Min Luo; De-Tian Huang; Pei-Zhong Liu; Hsuan-Ming Feng. An novel random forests and its application to the classification of mangroves remote sensing image. Multimedia Tools and Applications 2015, 75, 9707 -9722.
AMA StyleYan-Min Luo, De-Tian Huang, Pei-Zhong Liu, Hsuan-Ming Feng. An novel random forests and its application to the classification of mangroves remote sensing image. Multimedia Tools and Applications. 2015; 75 (16):9707-9722.
Chicago/Turabian StyleYan-Min Luo; De-Tian Huang; Pei-Zhong Liu; Hsuan-Ming Feng. 2015. "An novel random forests and its application to the classification of mangroves remote sensing image." Multimedia Tools and Applications 75, no. 16: 9707-9722.
The dynamic image segmentation algorithm with multiple stepwise evaluation machines was applied to resultant the new boundary from image contents. The concept of data fusion is also discussed in this research for making the good decision of image behavior by a 3D image describer. It achieves the high-understanding objects by merging some non-distinct image domains from the training patterns. Image describer contains expert knowledge to extract appropriate behaviors of the identified image patterns through the efficient dynamic image segmentation algorithm. The novel dynamic image segmentation algorithm is directly applied to explore recognitions of remote sensing images, where it can quickly choice the proper partition number of interesting image patterns area and determine their associated central positions. Due to the specific image intensity appropriately represent in the form of 3D description, an approximation object was dynamically generated with the image partition phase and merging stage to find appropriate 3D image describer. This 3D image describer explicitly presents its feature in diverse maps. Finally, the classification problems of three remote sensing images in computer simulations compared with both k-means and Fuzzy c-means (FCMs) methods. The measurement of misclassification error (ME) is selected to present the great results in various remote sensing images segmentation by the designed algorithm.
Ching-Yi Chen; Hsuan-Ming Feng; Hua-Ching Chen; Shiang-Min Jou. Dynamic image segmentation algorithm in 3D descriptions of remote sensing images. Multimedia Tools and Applications 2015, 75, 9723 -9743.
AMA StyleChing-Yi Chen, Hsuan-Ming Feng, Hua-Ching Chen, Shiang-Min Jou. Dynamic image segmentation algorithm in 3D descriptions of remote sensing images. Multimedia Tools and Applications. 2015; 75 (16):9723-9743.
Chicago/Turabian StyleChing-Yi Chen; Hsuan-Ming Feng; Hua-Ching Chen; Shiang-Min Jou. 2015. "Dynamic image segmentation algorithm in 3D descriptions of remote sensing images." Multimedia Tools and Applications 75, no. 16: 9723-9743.
Clustering algorithm is a crucial step before to analysis object’s feature in image applications. The adapt DB-PSO patterns clustering algorithms (ADPCA) combined the particle swarm optimization (PSO) clustering algorithm and adapt DB_index measuring methodology to efficiently decide the real number of clusters, cluster centers, and then to recognize the correct catalog even if there are exiting some cases in various shapes, multi-dimension, real life training patterns and image datasets. In general, the PSO is adapted for dealing complex and global optimization problems. The population-based evolutional PSO learning algorithm with the self-adapt mathematic index can fit the data vibration to perform the real criterion of homogeneity of neighboring pixels in many image vision and understanding cases. Owing to the purpose of generating automatic clustering algorithms, the specific fitness function contains the DB_validity measure to significantly improve resolutions of spatial information among the given training patterns. The computation of image DB_index is delivered to retrieve the specific objects by evaluating the characters of given patterns. The novel ADPCA actually indicate the homogeneity region of interesting pictures and eliminate small pieces of elements by the supports of DB index measure, which can be used to dynamically compute the maximal similarity and small difference of the discussed image patterns. Several artificial datasets include the three-dimensional dataset with five spherical clusters, two-dimensional patterns with three different sizes circles, one Chtree Fractal image patterns, one real life IRIS data and one grey level image data, which are given as training patterns to demonstrate the adaptation and efficiency of the ADPCA learning method. It presents that ADPCA determine the correct clustering number and suitable cluster position in different data clustering examples. Two image segmentation applications also show that ADPCA can achieve correct detection of subjects. In conclusion, several simulations compared with the traditional k-means algorithm demonstrate the great results of ADPCA learning machine.
Hua-Ching Chen; Hsuan-Ming Feng; Te-Hui Lin; Ching-Yi Chen; Yu-Xiang Zha. Adapt DB-PSO patterns clustering algorithms and its applications in image segmentation. Multimedia Tools and Applications 2015, 75, 15327 -15339.
AMA StyleHua-Ching Chen, Hsuan-Ming Feng, Te-Hui Lin, Ching-Yi Chen, Yu-Xiang Zha. Adapt DB-PSO patterns clustering algorithms and its applications in image segmentation. Multimedia Tools and Applications. 2015; 75 (23):15327-15339.
Chicago/Turabian StyleHua-Ching Chen; Hsuan-Ming Feng; Te-Hui Lin; Ching-Yi Chen; Yu-Xiang Zha. 2015. "Adapt DB-PSO patterns clustering algorithms and its applications in image segmentation." Multimedia Tools and Applications 75, no. 23: 15327-15339.
The arbitration inter frame space, Contention window minimum and Contention window maximum are some of the most important parameters of 802.11e, and the enhanced parameters tuning algorithm is applied for their adjustment. To achieve the high quality of service (QoS), priority combinations strategy with simpleness and effectiveness is proposed. In such a strategy, the internal competition of business analysis methods is used to detect the channel busy probability. Via different settings of the above parameters, the EPT reduces the conflict probability to complete the performance analysis while retreating the traffic business to the idle and zero states. Simulation environments are built for test and validation the better adapted regulation mechanism with the parameters.
Hua-Ching Chen; Hsuan-Ming Feng; Ben Bin Chen; Dong-Hui Guo. A Parameters Tuning Algorithm in Wireless Networks. 2014 IEEE Eighth International Conference on Software Security and Reliability-Companion 2014, 257 -260.
AMA StyleHua-Ching Chen, Hsuan-Ming Feng, Ben Bin Chen, Dong-Hui Guo. A Parameters Tuning Algorithm in Wireless Networks. 2014 IEEE Eighth International Conference on Software Security and Reliability-Companion. 2014; ():257-260.
Chicago/Turabian StyleHua-Ching Chen; Hsuan-Ming Feng; Ben Bin Chen; Dong-Hui Guo. 2014. "A Parameters Tuning Algorithm in Wireless Networks." 2014 IEEE Eighth International Conference on Software Security and Reliability-Companion , no. : 257-260.
A radial basis function neural networks (RBFNs) mobile robot control system is automatically developed with the image processing and learned by the bacterial foraging particle swarm optimization (BFPSO) algorithm in this paper. The image-based architecture of robot model is self-generated to travel the routing path in the dynamical and complicated environments. The visible omni-directional image sensors capture the surrounding environment to represent the behavior model of the mobile robot system. Three parameterize RBFNs model with the centers and spreads of each radial basis function, and the connection weights to solve the mobile robot path traveling and routing problems. Several free parameters of radial basis functions can be automatically tuned by the direct of the specified fitness function. In additional, the proper number of radial basis functions of the constructed RBFNs can be chosen by the defined fitness function which takes this factor into account. The desired multiple objectives of the RBFNs control system are proposed to simultaneously approach the shorter path and avoid the unexpected obstacles. Evaluations of PSO and BFPSO show that the developed RBFNs robot systems skip the obstacles and efficiently achieve the desired targets as soon as possible.
Shian Ming Joug; Hsuan Ming Feng; Dong Hui Guo. Self-Tuning RBFNs Mobile Robot Systems through Bacterial Foraging Particle Swarm Optimization Learning Algorithm. Applied Mechanics and Materials 2013, 284-287, 2128 -2136.
AMA StyleShian Ming Joug, Hsuan Ming Feng, Dong Hui Guo. Self-Tuning RBFNs Mobile Robot Systems through Bacterial Foraging Particle Swarm Optimization Learning Algorithm. Applied Mechanics and Materials. 2013; 284-287 ():2128-2136.
Chicago/Turabian StyleShian Ming Joug; Hsuan Ming Feng; Dong Hui Guo. 2013. "Self-Tuning RBFNs Mobile Robot Systems through Bacterial Foraging Particle Swarm Optimization Learning Algorithm." Applied Mechanics and Materials 284-287, no. : 2128-2136.
A novel MPEG-4 video cross-layer algorithm (MVCLA) is applied in the wireless network platform to approach the higher quality of service (QoS) in various prioritised traffic flow of MPEG-4 video applications. Even the popular availability of delivering the wireless network access data by the enhanced distributed coordination access (EDCA) mechanism function, the proposed MVCLA considers two variant conditions (i.e., data packets and network traffic load) to tune the contention-based parameters of cross-layers. The queue space does not effectively resume the transmission quality for the most important video media when the traffic flow is heavy. Our proposed regulation way dynamically retained the most important image traffic flow information in various conditions. The access control (AC) queue is flexibly allocated for the transmission purposes while the network channel is busy. The novel tuning algorithm is not only to ensure the high-speed for the most important packets to pull up the QoS level in several video transmission applications, but also efficiently provide the higher utilisation of the queue length. Finally, higher QoS of MPEG-4 video application is actually to verify the great simulation by the developed MVCLA.
Hua Ching Chen; Hsuan Ming Feng; Dong Hui Guo. Novel MPEG-4 video cross-layer algorithm in wireless networks. International Journal of Computational Science and Engineering 2013, 8, 171 .
AMA StyleHua Ching Chen, Hsuan Ming Feng, Dong Hui Guo. Novel MPEG-4 video cross-layer algorithm in wireless networks. International Journal of Computational Science and Engineering. 2013; 8 (2):171.
Chicago/Turabian StyleHua Ching Chen; Hsuan Ming Feng; Dong Hui Guo. 2013. "Novel MPEG-4 video cross-layer algorithm in wireless networks." International Journal of Computational Science and Engineering 8, no. 2: 171.
An artificial neural prediction system is automatically developed with the combinations of step wise regression analysis (SRA), dynamic learning and recursive-based particle swarm optimization (RPSO) learning algorithms. In the first stage, the SRA can be considered like a data filtering machine to choose two primary factors from 20 channel technical indexes as input variables of the RBFNs system. Then, an efficient dynamic learning algorithm is applied to sequentially generate RBFs functions from training data set, where it can efficiently determine the proper number of RBFs’ centers and their associated positions. It can be exploited to forecast appropriate behaviors of the wanted identified financial time series data. While characteristics of training data set are automatically mined and generated by the proposed dynamic learning algorithm, architecture of the RBFNs prediction system is initially represented with collected information. Moreover, the RPSO learning scheme with the hybrid particle swarm optimization (PSO) and recursive least-squares (RLS) learning methods are applied to extract those appropriate parameters of the RBFNs prediction system. The RBFNs prediction systems are implemented in data analysis, module generation and price trend of the financial time series data. It not only automatically determines proper RBFs number but also fast approach the desired target in actual trading of Taiwan stock index (TAIEX). Computer simulations in training and testing phases of historic TAIEX are compared with other learning methods, which illustrate our great performance not only increases the accuracy of the stock price prediction but also improves the win rate in the trend of TAIEX.
Hsuan-Ming Feng; Hsiang-Chai Chou. Evolutional RBFNs prediction systems generation in the applications of financial time series data. Expert Systems with Applications 2011, 38, 8285 -8292.
AMA StyleHsuan-Ming Feng, Hsiang-Chai Chou. Evolutional RBFNs prediction systems generation in the applications of financial time series data. Expert Systems with Applications. 2011; 38 (7):8285-8292.
Chicago/Turabian StyleHsuan-Ming Feng; Hsiang-Chai Chou. 2011. "Evolutional RBFNs prediction systems generation in the applications of financial time series data." Expert Systems with Applications 38, no. 7: 8285-8292.
The novel particle swarm optimization (PSO) learning algorithm is applied to automatically generate the fuzzy systems with the image processing technology in achieving the adaptability of the embedded mobile robot. The omni-directional image mathematical model for the mobile robot system is established to represent the indoor environment. The embedded fuzzy control rules are automatically extracted by the direct of the flexible fitness function for multiple objectives of avoiding obstacles, selecting suitable fuzzy rules and approaching the desired targets at the same time. The illustrated examples with various initial positions for the discussed environment map containing the defined block is applied to demonstrate that the proposed mobile robot with the selected fuzzy rules can overcome the obstacles and achieve the targets as soon as possible.
Hsuan-Ming Feng; Hua-Ching Chen; Dong-Hui Guo. PSO-based fuzzy image mobile robot systems design. 2011 IEEE International Conference on Anti-Counterfeiting, Security and Identification 2011, 101 -105.
AMA StyleHsuan-Ming Feng, Hua-Ching Chen, Dong-Hui Guo. PSO-based fuzzy image mobile robot systems design. 2011 IEEE International Conference on Anti-Counterfeiting, Security and Identification. 2011; ():101-105.
Chicago/Turabian StyleHsuan-Ming Feng; Hua-Ching Chen; Dong-Hui Guo. 2011. "PSO-based fuzzy image mobile robot systems design." 2011 IEEE International Conference on Anti-Counterfeiting, Security and Identification , no. : 101-105.
The evolutionary particle swarm optimization (PSO) learning algorithm with the image processing technology is proposed to efficiently generate the fuzzy systems for achieving the control adaptability of the embedded mobile robot. The omni-directional image model of the mobile robot system is established to represent the entire tracking environment. The fuzzy control rules are automatically extracted by the defined flexible fitness function for multiple objectives in avoiding obstacles, selecting suitable fuzzy rules and approaching toward the desired targets at the same time. The illustrated examples with various initial positions and different blocks sizes are demonstrated that the selected fuzzy rules can overcome the obstacles and achieve the targets as soon as possible.
Hua-Ching Chen; Hsuan-Ming Feng; Dong-Hui Guo. Evolutionary Learning Mobile Robot Fuzzy Systems Design. Lecture Notes in Electrical Engineering 2011, 98, 29 -36.
AMA StyleHua-Ching Chen, Hsuan-Ming Feng, Dong-Hui Guo. Evolutionary Learning Mobile Robot Fuzzy Systems Design. Lecture Notes in Electrical Engineering. 2011; 98 ():29-36.
Chicago/Turabian StyleHua-Ching Chen; Hsuan-Ming Feng; Dong-Hui Guo. 2011. "Evolutionary Learning Mobile Robot Fuzzy Systems Design." Lecture Notes in Electrical Engineering 98, no. : 29-36.