Browsing by Author "Lin, Cheng-Jian"

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A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control
(2008-10-01)
Chen, Cheng-Hung
;
Lin, Cheng-Jian
;
Lin, Chin-Teng
;
資訊工程學系
;
電控工程研究所
This study presents a functional-link-based neurofuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Furthermore, results for the universal approximator and a convergence analysis of the FLNFN model are proven. Finally, the FLNFN model is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed FLNFN model.
A Hybrid of Bacterial Foraging Optimization and Particle Swarm Optimization for Evolutionary Neural Fuzzy Classifier
(2014-09-01)
Chen, Cheng-Hung
;
Su, Miin-Tsair
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Lin, Cheng-Jian
;
Lin, Chin-Teng
;
交大名義發表
This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method performs local search through the chemotactic movement operation of bacterial foraging whereas the global search over the entire search space is accomplished by a particle swarm operator. The NFC model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. Finally, the proposed neural fuzzy classifier with bacterial foraging particle swarm optimization (NFC-BFPSO) is adopted in several classification applications. Experimental results have demonstrated that the proposed NFC-BFPSO method can outperform other methods.
A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks
(2008)
Chen, Cheng-Hung
;
Liu, Yong-Cheng
;
Lin, Cheng-Jian
;
Lin, Chin-Teng
;
電控工程研究所
This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. Finally, the proposed functional-link-based neural fuzzy network with cultural cooperative particle swarm optimization (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.
A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications
(2009-01-01)
Lin, Cheng-Jian
;
Chen, Cheng-Hung
;
Lin, Chin-Teng
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. The FLNFN model can generate the consequent part of a nonlinear combination of input variables. Finally, the proposed FLNFN with CCPSO (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems.
A hybrid of fuzzy theory and quadratic function for estimating and refining transmission map
(2019-01-01)
Jhang, Jyun-Yu
;
Lin, Cheng-Jian
;
Young, Kuu-Young
;
Lin, Chin-Teng
;
電控工程研究所
In photographs captured in outdoor environments, particles in the air cause light attenuation and degrade image quality. This effect is especially obvious in hazy environments. In this study, a fuzzy theory is proposed to estimate the transmission map of a single image. To overcome the problem of oversaturation in dehazed images, a quadratic-function-based method is proposed to refine the transmission map. In addition, the color vector of the atmospheric light is estimated using the top 1% of the brightest light area. Finally, the dehazed image is reconstructed using the transmission map and the estimated atmospheric light. Experimental results demonstrate that the proposed hybrid method performs better than the other existing methods in terms of color oversaturation, visibility, and quantitative evaluation.
A recurrent functional-link-based neural fuzzy system and its applications
(2007)
Chen, Cheng-Hung
;
Lin, Cheng-Jian
;
Lin, Chin-Teng
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, a recurrent functional-link-based neural fuzzy system (RFLNFS) is proposed for prediction of time sequence and skin color detection. The proposed RFLNFS model uses functional link neural network as the consequent part of fuzzy rules. The RFLNFS model can generate the consequent part of a nonlinear combination of the input variables. The recurrent network is embedded in the RFLNFS by adding feedback connections in the second layer, where the feedback units act as memory elements. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. Finally, the RFLNFS is applied to two simulations. The simulation results of the dynamic system modeling have shown that the RFLNFS model can solve the temporal problem and the RFLNFS model has superior performance than other models.
A Rule-Based Symbiotic MOdified Differential Evolution for Self-Organizing Neuro-Fuzzy Systems
(2011-12-01)
Su, Miin-Tsair
;
Chen, Cheng-Hung
;
Lin, Cheng-Jian
;
Lin, Chin-Teng
;
電機工程學系
This study proposes a Rule-Based Symbiotic MOdified Differential Evolution (RSMODE) for Self-Organizing Neuro-Fuzzy Systems (SONFS). The RSMODE adopts a multi-subpopulation scheme that uses each individual represents a single fuzzy rule and each individual in each subpopulation evolves separately. The proposed RSMODE learning algorithm consists of structure learning and parameter learning for the SONFS model. The structure learning can determine whether or not to generate a new rule-based subpopulation which satisfies the fuzzy partition of input variables using the entropy measure. The parameter learning combines two strategies including a subpopulation symbiotic evolution and a modified differential evolution. The RSMODE can automatically generate initial subpopulation and each individual in each subpopulation evolves separately using a modified differential evolution. Finally, the proposed method is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed RSMODE learning algorithm. (C) 2011 Elsevier B. V. All rights reserved.
An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation
(2008-04-01)
Chen, Cheng-Hung
;
Lin, Cheng-Jian
;
Lin, Chin-Teng
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, a quantum neuro-fuzzy classifier (QNFC) for classification applications is proposed. The proposed QNFC model is a five-layer structure, which combines the compensatory-based fuzzy reasoning method with the traditional Takagi-Sugeno-Kang (TSK) fuzzy model. The compensatory-based fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Layer 2 of the QNFC model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), quantum fuzzy entropy and the backpropagation algorithm, is also proposed. The proposed SCA method is a fast, one-pass algorithm that dynamically estimates the number of clusters in an input data space. Quantum fuzzy entropy is employed to evaluate the information on pattern distribution in the pattern space. With this information, we can determine the number of quantum levels. The backpropagation algorithm is used to tune the adjustable parameters. The simulation results have shown that (1) the QNFC model converges quickly; (2) the QNFC model has a higher correct classification rate than other models.
An entropy-based quantum neuro-fuzzy inference system for classification applications
(2007-08-01)
Lin, Cheng-Jian
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Chung, I-Fang
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Chen, Cheng-Hung
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電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, an entropy-based quantum neuro-fuzzy inference system (EQNFIS) for classification applications is proposed. The EQNFIS model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the EQNFIS model contains quantum membership functions, which are multilevel activation functions. Each quantum membership function is composed of the sum of sigmoid functions shifted by quantum intervals. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), quantum fuzzy entropy, and the backpropagation algorithm, is also proposed. The proposed SCA method is a fast, one-pass algorithm that dynamically estimates the number of clusters in an input data space. Quantum fuzzy entropy is employed to evaluate the information on pattern distribution in the pattern space. With this information, we can determine the number of quantum levels. The backpropagation algorithm is used to tune the adjustable parameters. Simulations were conducted to show the performance and applicability of the proposed model. (C) 2006 Elsevier B.V. All rights reserved.
Classification and medical diagnosis using wavelet-based fuzzy neural networks
(2008-03-01)
Lin, Cheng-Jian
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Chen, Chenghung
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Lee, Chiyung
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電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, we propose a Wavelet-based Fuzzy Neural Network (WFNN) for classification and medical diagnosis. The proposed WFNN integrates the wavelet transform functions into a fuzzy system. We use non-orthogonal and compactly supported functions as the wavelet neural network (WNN) bases. The goal of the WFNN model combined with WNN is to improve the accuracy of function approximation. An on-line structure/parameter learning algorithm is used in the WFNN. Structure learning is based on the input partitions to determine the number of fuzzy rules and wavelet functions, and parameter learning is based on the supervised gradient descent method to adjust the shape of the membership functions and the connection weights of the wavelet neural networks. Computer simulations were conducted to test the performance and applicability of the proposed system.
Cooperative Carrying Control for Multi-Evolutionary Mobile Robots in Unknown Environments
(2019-03-06)
Jhang, Jyun-Yu
;
Lin, Cheng-Jian
;
Young, Kuu-Young
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This study provides an effective cooperative carrying and navigation control method for mobile robots in an unknown environment. The manager mode switches between two behavioral control modes-wall-following mode (WFM) and toward-goal mode (TGM)-based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller (IT2FNC) based on a dynamic group differential evolution (DGDE) is proposed to realize the carrying control and WFM control for mobile robots. The proposed DGDE uses a hybrid method that involves a group concept and an improved differential evolution to overcome the drawbacks of the traditional differential evolution algorithm. A reinforcement learning strategy was adopted to develop an adaptive WFM control and achieve cooperative carrying control for mobile robots. The experimental results demonstrated that the proposed DGDE is superior to other algorithms at using WFM control. Moreover, the experimental results demonstrate that the proposed method can complete the task of cooperative carrying, and can realize navigation control to enable the robot to reach the target location.
Dynamic system identification using a recurrent compensatory fuzzy neural network
(2008-10-01)
Lee, Chi-Yung
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Lin, Cheng-Jian
;
Chen, Cheng-Hung
;
Chang, Chun-Lung
;
電控工程研究所
This Study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.
Efficient immune-based particle swarm optimization learning for neuro-fuzzy networks design
(2008-09-01)
Lin, Cheng-Jian
;
Chen, Cheng-Hung
;
Lee, Chi-Yung
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電控工程研究所
;
Institute of Electrical and Control Engineering
In order to enhance the immune algorithm (IA) performance and find the optimal solution when dealing with difficult problems, we propose an efficient immune-based particle swarm optimization (IPSO) for use in TSK-type neuro-fuzzy networks for solving the identification and prediction problems. The proposed IPSO combines the immune algorithm (IA) and particle swarm optimization (PSO) to perform parameter learning. The IA uses the clonal selection principle, such that antibodies between others of high similar degree are affected, and these antibodies, after the process, will have higher quality, accelerating the search and increasing the global search capacity. The PSO algorithm has proved to be very effective for solving global optimization. It is not only a recently invented high-performance optimizer that is easy to understand and implement, but it also requires little computational bookkeeping and generally only a few lines of code. Hence, we employed the advantages of PSO to improve the mutation mechanism of immune algorithm. Experiments with synthetic and real data sets have performed in order to show the applicability of the proposed approach and also to compare with other methods in the literature.
Efficient reinforcement hybrid evolutionary learning for recurrent wavelet-based neuro-fuzzy systems
(2007)
Chen, Cheng-Hung
;
Lin, Cheng-Jian
;
Lee, Chi-Yung
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a recurrent wavelet-based neuro-fuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA), performs the structure/parameter learning for dynamically constructing the RWNFS. That, is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. An illustrative example was conducted to show the performance and applicability of the proposed R-HELA method.
Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems
(2008-12-01)
Lin, Cheng-Jian
;
Chen, Cheng-Hung
;
Lin, Chin-Teng
;
資訊工程學系
;
電控工程研究所
This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA is that it is based on evolutionary algorithms that can determine the number of fuzzy rules and adjust-the NFIS parameters. The SEELA consists of structure learning and parameter learning. The structure learning attempts to determine the number of fuzzy rules. A subgroup symbiotic evolution is adopted to yield several variable fuzzy systems, and an elite-based structure strategy is adopted to find a suitable number of fuzzy rules for solving a problem. The parameter learning is to adjust parameters of the NFIS. It is a hybrid evolutionary algorithm of cooperative particle swarm optimization (CPSO) and cultural algorithm, called cultural CPSO (CCPSO). The CCPSO, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Experimental results demonstrate that the proposed method performs well in predicting time series and solving nonlinear control problems.
FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control
(2018-08-01)
Jhang, Jyun-Yu
;
Tang, Kuang-Hui
;
Huang, Chuan-Kuei
;
Lin, Cheng-Jian
;
Young, Kuu-Young
This study used Xilinx Field Programmable Gate Arrays (FPGAs) to implement a functional neuro-fuzzy network (FNFN) for solving nonlinear control problems. A functional link neural network (FLNN) was used as the consequent part of the proposed FNFN model. This study adopted the linear independent functions and the orthogonal polynomials in a functional expansion of the FLNN. Thus, the design of the FNFN model could improve the control accuracy. The learning algorithm of the FNFN model was divided into structure learning and parameter learning. The entropy measurement was adopted in the structure learning to determine the generated new fuzzy rule, whereas the gradient descent method in the parameter learning was used to adjust the parameters of the membership functions and the weights of the FLNN. In order to obtain high speed operation and real-time application, a very high speed integrated circuit hardware description language (VHDL) was used to design the FNFN controller and was implemented on FPGA. Finally, the experimental results demonstrated that the proposed hardware implementation of the FNFN model confirmed the viability in the temperature control of a water bath and the backing control of a car.
Navigation Control of Mobile Robots Using an Interval Type-2 Fuzzy Controller Based on Dynamic-group Particle Swarm Optimization
(2018-10-01)
Jhang, Jyun-Yu
;
Lin, Cheng-Jian
;
Lin, Chin-Teng
;
Young, Kuu-Young
;
電機工程學系
This paper presents an effective navigation control method for mobile robots in an unknown environment. The proposed behavior manager (BM) switches between two behavioral control patterns, wall-following behavior (WFB) and toward-goal behavior (TGB), based on the relationship between the mobile robot and the unknown environment. An interval type-2 fuzzy neural controller with a dynamic-group particle swarm optimization (DGPSO) algorithm is proposed to provide WFB control and obstacle avoidance for mobile robots. In the WFB learning process, the input signal of a controller is the distance between the wall and the sonar sensors, and its output signal is the speed of two wheels of a mobile robot. A fitness function, which operates on the total distance traveled by the mobile robot, distance from the side wall, angle to the side wall, and moving speed, evaluates the WFB performance of the mobile robot. In addition, an escape mechanism is proposed to avoid a dead cycle. Experimental results reveal that the proposed DGPSO is superior to other methods in WFB and navigation control.
Nonlinear System Control Using Adaptive Neural Fuzzy Networks Based on a Modified Differential Evolution
(2009-07-01)
Chen, Cheng-Hung
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Lin, Cheng-Jian
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Lin, Chin-Teng
;
資訊工程學系
;
電控工程研究所
This study presents an adaptive neural fuzzy network (ANFN) controller based on a modified differential evolution (MODE) for solving control problems. The proposed ANFN controller adopts a functional link neural network as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN controller is a nonlinear combination of input variables. The proposed MODE learning algorithm adopts an evolutionary learning method to optimize the controller parameters. For design optimization, a new criterion is introduced. A hardware-in-the loop control technique is developed and applied to the designed ANFN controller using the MODE learning algorithm. The proposed ANFN controller with the MODE learning algorithm (ANFN-MODE) is used in two practical applications-the planetary-train-type inverted pendulum system and the magnetic levitation system. The experiment is developed in a real-time visual simulation environment. Experimental results of this study have demonstrated the robustness and effectiveness of the proposed ANFN-MODE controller.
Nonlinear System Control Using Functional-Link-Based Neuro-Fuzzy Network Model Embedded with Modified Particle Swarm Optimizer
(2012-03-01)
Su, Miin-Tsair
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Lin, Chin-Teng
;
Hsu, Sheng-Chih
;
Li, Dong-Lin
;
Lin, Cheng-Jian
This study presents an evolutionary neural fuzzy system (NFS) for nonlinear system control. The proposed NFS model uses functional link neural networks (FLNNs) as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. A learning algorithm, which consists of structure learning and parameter learning, is presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the particle swarm optimization (PSO) algorithm, can adjust the shape of the membership function and the corresponding weighting of the FLNN. The distance-based mutation operator, which strongly encourages a global search giving the particles more chance of converging to the global optimum, is introduced. The simulation results have shown the proposed method can improve the searching ability and is very suitable for the nonlinear system control applications.
Reinforcement hybrid evolutionary learning for recurrent wavelet-based neurofuzzy systems
(2007-08-01)
Lin, Cheng-Jian
;
Hsu, Yung-Chi
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a recurrent wavelet-based neurofuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA), and the modified variable-length genetic algorithm (MVGA) performs the structure/parameter learning for dynamically constructing the RWNFS. That is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. Illustrative examples were conducted to show the performance and applicability of the proposed R-HELA method.