Dna computing based genetic algorithm, applications in industrial process modeling and control
Tao, Jili
Dna computing based genetic algorithm : applications in industrial process modeling and control [Documento eletrónico] / by Jili Tao, Ridong Zhang, Yong Zhu. - Singapore : Springer Nature Singapore : Springer , 2020 . - IX, 274 p. : il.. 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. .
Clicar aqui para aceder a um recurso externo
ISBN 978-981-15-5403-2
Mathematics
Control engineering
Artificial intelligence
LCC QA71-90
Dna computing based genetic algorithm : applications in industrial process modeling and control [Documento eletrónico] / by Jili Tao, Ridong Zhang, Yong Zhu. - Singapore : Springer Nature Singapore : Springer , 2020 . - IX, 274 p. : il.. 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. .
Clicar aqui para aceder a um recurso externo
ISBN 978-981-15-5403-2
Mathematics
Control engineering
Artificial intelligence
LCC QA71-90
