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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.
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.
This book is based on the authors' research on the stabilization
and fault-tolerant control of batch processes, which are
flourishing topics in the field of control system engineering. It
introduces iterative learning control for linear/nonlinear
single/multi-phase batch processes; iterative learning optimal
guaranteed cost control; delay-dependent iterative learning
control; and iterative learning fault-tolerant control for
linear/nonlinear single/multi-phase batch processes. Providing
important insights and useful methods and practical algorithms that
can potentially be applied in batch process control and
optimization, it is a valuable resource for researchers,
scientists, and engineers in the field of process system
engineering and control engineering.
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