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DNA computing based genetic algorith...

Tao, Jili.

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DNA computing based genetic algorithm = applications industrial process modeling and control /

Record Type:	Electronic resources : Monograph/item
Title/Author:	DNA computing based genetic algorithm/ by Jili Tao, Ridong Zhang, Yong Zhu.
Reminder of title:	applications industrial process modeling and control /
Author:	Tao, Jili.
other author:	Zhang, Ridong.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	ix, 274 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Genetic algorithms. -
Online resource:	https://doi.org/10.1007/978-981-15-5403-2
ISBN:	9789811554032

DNA computing based genetic algorithm = applications industrial process modeling and control /
Tao, Jili.

DNA computing based genetic algorithmapplications industrial process modeling and control /[electronic resource] :by Jili Tao, Ridong Zhang, Yong Zhu. - Singapore :Springer Singapore :2020. - ix, 274 p. :ill., digital ;24 cm.

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.

ISBN: 9789811554032

Standard No.: 10.1007/978-981-15-5403-2doiSubjects--Topical Terms:

533907
Genetic algorithms.

LC Class. No.: QA402.5 / .T36 2020

Dewey Class. No.: 519.625

DNA computing based genetic algorithm = applications industrial process modeling and control /
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505 0 $a 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.
520 $a 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.
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