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Record Nr. |
UNISA996418262103316 |
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Autore |
Tao Jili |
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Titolo |
DNA Computing Based Genetic Algorithm [[electronic resource] ] : Applications in Industrial Process Modeling and Control / / by Jili Tao, Ridong Zhang, Yong Zhu |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (IX, 274 p. 187 illus., 108 illus. in color.) |
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Disciplina |
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Soggetti |
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Computer mathematics |
Control engineering |
Artificial intelligence |
Computational Science and Engineering |
Control and Systems Theory |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. . |
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