Link on this page

<E-Book>
DNA Computing Based Genetic Algorithm : Applications in Industrial Process Modeling and Control / by Jili Tao, Ridong Zhang, Yong Zhu

Edition 1st ed. 2020.
Publisher (Singapore : Springer Nature Singapore : Imprint: Springer)
Year 2020
Language English
Size IX, 274 p. 187 illus., 108 illus. in color : online resource
Authors *Tao, Jili author
Zhang, Ridong author
Zhu, Yong author
SpringerLink (Online service)
Subjects LCSH:Mathematics -- Data processing  All Subject Search
LCSH:Control engineering
LCSH:Artificial intelligence
FREE:Computational Science and Engineering
FREE:Control and Systems Theory
FREE:Artificial Intelligence
Notes 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
TOC

Hide book details.

E-Book オンライン 電子ブック

Springer eBooks 9789811554032
電子リソース
EB00226861

Hide details.

Material Type E-Book
Classification LCC:QA71-90
DC23:003.3
ID 4000135298
ISBN 9789811554032

 Similar Items