UID:
almahu_9948436032402882
Format:
IX, 274 p. 187 illus., 108 illus. in color.
,
online resource.
Edition:
1st ed. 2020.
ISBN:
9789811554032
Content:
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. .
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9789811554025
Additional Edition:
Printed edition: ISBN 9789811554049
Additional Edition:
Printed edition: ISBN 9789811554056
Language:
English
Subjects:
Computer Science
,
Mathematics
DOI:
10.1007/978-981-15-5403-2
URL:
https://doi.org/10.1007/978-981-15-5403-2
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