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[null,"1.","Record Nr.",null,"UNISA996418262103316"]
[null,null,"Autore",null,"Tao Jili"]
[null,null,"Titolo",null,"DNA Computing Based Genetic Algorithm [[electronic resource] ] : Applications in Industrial Process Modeling and Control  / / by Jili Tao, Ridong Zhang, Yong Zhu"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"981-15-5403-X"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (IX, 274 p. 187 illus., 108 illus. in color.)"]
[null,null,"Disciplina",null,"519.7"]
[null,null,"Soggetti",null,"Computer mathematics"]
[{"$nulls":4},"Control engineering"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computational Science and Engineering"]
[{"$nulls":4},"Control and Systems Theory"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Introduction -- DNA computing based RNA-GA -- DNA double-helix based hybrid genetic algorithm -- DNA computing based multi-objective genetic algorithm -- Parameter identification and optimization for chemical process -- RBF neural network for nonlinear SISO system -- T-S Fuzzy neural network for nonlinear SISO system -- PCA & GA based ARX plus RBF Modeling for Nonlinear DPS -- GA based predictive control design -- MOGA based PID controller design -- Concluding Remarks."]
[null,null,null,"Sommario/riassunto","This book focuses on the implementation, evaluation and application of DNA/RNA-based genetic algorithms in connection with neural network modeling, fuzzy control, the Q-learning algorithm and CNN deep learning classifier. It presents several DNA/RNA-based genetic algorithms and their modifications, which are tested using benchmarks, as well as detailed information on the implementation steps and program code. In addition to single-objective optimization, here genetic algorithms are also used to solve multi-objective optimization for neural network modeling, fuzzy control, model predictive control and PID control. In closing, new topics such as Q-learning and CNN are introduced. The book offers a valuable reference guide for researchers and designers in system modeling and control, and for senior "]
[{"$nulls":4},"undergraduate and graduate students at colleges and universities. ."]