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DNA computing based genetic algorith...
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SpringerLink (Online service)
DNA computing based genetic algorithmapplications industrial process modeling and control /
Record Type:
Electronic resources : Monograph/item
Title/Author:
DNA computing based genetic algorithmby 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.
Contained By:
Springer Nature eBook
Subject:
Genetic algorithms.
Online resource:
https://doi.org/10.1007/978-981-15-5403-2
ISBN:
9789811554032$q(electronic bk.)
DNA computing based genetic algorithmapplications industrial process modeling and control /
Tao, Jili.
DNA computing based genetic algorithm
applications industrial process modeling and control /[electronic resource] :by Jili Tao, Ridong Zhang, Yong Zhu. - Singapore :Springer Singapore :2020. - ix, 274 p. :ill., digital ;24 cm.
Introduction -- DNA computing based RNA-GA -- DNA double-helix based hybrid genetic algorithm -- DNA computing based multi-objective genetic algorithm -- Parameter identification and optimization for chemical process -- RBF neural network for nonlinear SISO system -- T-S Fuzzy neural network for nonlinear SISO system -- PCA & GA based ARX plus RBF Modeling for Nonlinear DPS -- GA based predictive control design -- MOGA based PID controller design -- Concluding Remarks.
This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior undergraduate and graduate students at colleges and universities.
ISBN: 9789811554032$q(electronic bk.)
Standard No.: 10.1007/978-981-15-5403-2doiSubjects--Topical Terms:
182939
Genetic algorithms.
LC Class. No.: QA402.5 / .T36 2020
Dewey Class. No.: 519.625
DNA computing based genetic algorithmapplications industrial process modeling and control /
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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.
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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. 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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