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＜電子ブック＞
DNA Computing Based Genetic Algorithm : Applications in Industrial Process Modeling and Control / by Jili Tao, Ridong Zhang, Yong Zhu

版	1st ed. 2020.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2020
本文言語	英語
大きさ	IX, 274 p. 187 illus., 108 illus. in color : online resource
著者標目	*Tao, Jili author Zhang, Ridong author Zhu, Yong author SpringerLink (Online service)
件　名	LCSH:Mathematics -- Data processing  全ての件名で検索 LCSH:Control engineering LCSH:Artificial intelligence FREE:Computational Science and Engineering FREE:Control and Systems Theory FREE:Artificial Intelligence
一般注記	Introduction -- DNA computing based RNA-GA -- DNA double-helix based hybrid genetic algorithm -- DNA computing based multi-objective genetic algorithm -- Parameter identification and optimization for chemical process -- RBF neural network for nonlinear SISO system -- T-S Fuzzy neural network for nonlinear SISO system -- PCA & GA based ARX plus RBF Modeling for Nonlinear DPS -- GA based predictive control design -- MOGA based PID controller design -- Concluding Remarks This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities. HTTP:URL=https://doi.org/10.1007/978-981-15-5403-2

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