Browsing by Author "Lee, Chi-Yung"

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Dynamic system identification using a recurrent compensatory fuzzy neural network
(2008-10-01)
Lee, Chi-Yung
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Lin, Cheng-Jian
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Chen, Cheng-Hung
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Chang, Chun-Lung
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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
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Chen, Cheng-Hung
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Lee, Chi-Yung
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電控工程研究所
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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
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Lin, Cheng-Jian
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Lee, Chi-Yung
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電控工程研究所
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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.
Wafer Scale Phase-Engineered 1T-and 2H-MoSe2/Mo Core-Shell 3D-Hierarchical Nanostructures toward Efficient Electrocatalytic Hydrogen Evolution Reaction
(2016-11-23)
Qu, Yindong
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Medina, Henry
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Wang, Sheng-Wen
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Wang, Yi-Chung
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Chen, Chia-Wei
The necessity for new sources for greener and cleaner energy production to replace the existing ones has been increasingly growing in recent years. Of those new sources, the hydrogen evolution reaction has a large potential. In this work, for the first time, MoSe2/Mo core-shell 3D-hierarchical nanostructures are created, which are derived from the Mo 3D-hierarchical nanostructures through a low-temperature plasma-assisted selenization process with controlled shapes grown by a glancing angle deposition system.