000			01774nam a22003015i 4500
001			90957
005			20231026102312.0
010			_a978-981-15-5403-2 _dcompra
090			_a90957
100			_a20231023d2020 k||y0pory50 ba
101	0		_aeng
102			_aSG
200	1		_aDna computing based genetic algorithm _bDocumento eletrónico _eapplications in industrial process modeling and control _fby Jili Tao, Ridong Zhang, Yong Zhu
210			_aSingapore _cSpringer Nature Singapore _cSpringer _d2020
215			_aIX, 274 p. _cil.
303			_aThis 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. .
606			_aMathematics _xData processing
606			_aControl engineering
606			_aArtificial intelligence
680			_aQA71-90
700	1		_aTao _bJili
701	1		_aZhang _bRidong
701	1		_aZhu _bYong
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-981-15-5403-2
942			_2lcc _cF _n0