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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 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.
| Autorius: | Jili Tao, Yong Zhu, Ridong Zhang, |
| Leidėjas: | Springer Nature Singapore |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 284 |
| ISBN-10: | 9811554056 |
| ISBN-13: | 9789811554056 |
| Formatas: | 235 x 155 x 16 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „DNA Computing Based Genetic Algorithm: Applications in Industrial Process Modeling and Control“
