國立虎尾科技大學 |

DNA Computing Based Genetic Algorithm = Applications in Industrial Process Modeling and Control /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	DNA Computing Based Genetic Algorithm/ by Jili Tao, Ridong Zhang, Yong Zhu.
Reminder of title:	Applications in Industrial Process Modeling and Control /
Author:	Tao, Jili.
other author:	Zhang, Ridong.
Description:	IX, 274 p. 187 illus., 108 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer mathematics. -
Online resource:	https://doi.org/10.1007/978-981-15-5403-2
ISBN:	9789811554032

DNA Computing Based Genetic Algorithm = Applications in Industrial Process Modeling and Control /
Tao, Jili.

DNA Computing Based Genetic AlgorithmApplications in Industrial Process Modeling and Control /[electronic resource] :by Jili Tao, Ridong Zhang, Yong Zhu. - 1st ed. 2020. - IX, 274 p. 187 illus., 108 illus. in color.online resource.

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:

1199796
Computer mathematics.

LC Class. No.: QA71-90

Dewey Class. No.: 004

DNA Computing Based Genetic Algorithm = Applications in Industrial Process Modeling and Control /
LDR:02757nam a22003975i 4500 001 1020931
003 DE-He213
005 20200705213955.0
007 cr nn 008mamaa
008 210318s2020 si | s |||| 0|eng d
020 $a 9789811554032 $9 978-981-15-5403-2
024 7 $a 10.1007/978-981-15-5403-2 $2 doi
035 $a 978-981-15-5403-2
050 4 $a QA71-90
072 7 $a PDE $2 bicssc
072 7 $a COM014000 $2 bisacsh
072 7 $a PDE $2 thema
082 0 4 $a 004 $2 23
100 1 $a Tao, Jili. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1316530
245 1 0 $a DNA Computing Based Genetic Algorithm $h [electronic resource] : $b Applications in Industrial Process Modeling and Control / $c by Jili Tao, Ridong Zhang, Yong Zhu.
250 $a 1st ed. 2020.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a IX, 274 p. 187 illus., 108 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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. .
650 0 $a Computer mathematics. $3 1199796
650 0 $a Control engineering. $3 1249728
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computational Science and Engineering. $3 670319
650 2 4 $a Control and Systems Theory. $3 1211358
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Zhang, Ridong. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1228392
700 1 $a Zhu, Yong. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1247156
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811554025
776 0 8 $i Printed edition: $z 9789811554049
776 0 8 $i Printed edition: $z 9789811554056
856 4 0 $u https://doi.org/10.1007/978-981-15-5403-2
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)