DNA Computing Based Genetic Algorithm

Applications in Industrial Process Modeling and Control

Häftad, Engelska, 2021

AvJili Tao,Ridong Zhang,Yong Zhu

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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.

Produktinformation

Utgivningsdatum2021-07-01
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
Antal sidor274
FörlagSpringer Verlag, Singapore
ISBN9789811554056

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Jili Tao received her B.Sc. and Ph.D. from Central South University and Zhejiang University, China, in 2004 and 2007, respectively. She is currently a Professor at the Institute of Ningbo Technology, Ningbo, China. Her research interests include intelligent optimization, modeling and its applications to electronic system design and control system design. Ridong Zhang received his Ph.D. in Control Science and Engineering from Zhejiang University, Hangzhou, China, in 2007. From 2007 to 2015, he was a Full Professor with the Institute of Information and Control, Hangzhou Dianzi University, Hangzhou. Since 2015, he has been a Visiting Professor at the Chemical and Biomolecular Engineering Department, Hong Kong University of Science and Technology. His research interests include process modeling, model predictive control and nonlinear systems. Yong Zhu, received M.Sc. degrees from HangZhou DianZi University, China, in 2004. He is currently a lecturer in the Institute of Ningbo technology, Ningbo, China. Meanwhile he has been a Ph.D. candidate in Ningbo University. His present research interests in electronic system design and advanced control system design.

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.