Intelligent Control techniques are becoming important tools in both academia and industry. Methodologies developed in the field of soft-computing, such as neural networks, fuzzy systems and evolutionary computation, can lead to accommodation of more complex processes, improved performance and considerable time savings and cost reductions.
Intelligent Control techniques are becoming important tools in both academia and industry. Methodologies developed in the field of soft-computing, such as neural networks, fuzzy systems and evolutionary computation, can lead to accommodation of more complex processes, improved performance and considerable time savings and cost reductions.
* Chapter 1: An overview of nonlinear identification and control with fuzzy systems * Chapter 2: An overview of nonlinear identification and control with neural networks * Chapter 3: Multi-objective evolutionary computing solutions for control and system identification * Chapter 4: Adaptive local linear modelling and control of nonlinear dynamical systems * Chapter 5: Nonlinear system identification with local linear neuro-fuzzy models * Chapter 6: Gaussian process approaches to nonlinear modelling for control * Chapter 7: Neuro-fuzzy model construction, design and estimation * Chapter 8: A neural network approach for nearly optimal control of constrained nonlinear systems * Chapter 9: Reinforcement learning for online control and optimisation * Chapter 10: Reinforcement learning and multi-agent control within an internet environment * Chapter 11: Combined computational intelligence and analytical methods in fault diagnosis * Chapter 12: Application of intelligent control to autonomous search of parking place and parking of vehicles * Chapter 13: Applications of intelligent control in medicine
* Chapter 1: An overview of nonlinear identification and control with fuzzy systems * Chapter 2: An overview of nonlinear identification and control with neural networks * Chapter 3: Multi-objective evolutionary computing solutions for control and system identification * Chapter 4: Adaptive local linear modelling and control of nonlinear dynamical systems * Chapter 5: Nonlinear system identification with local linear neuro-fuzzy models * Chapter 6: Gaussian process approaches to nonlinear modelling for control * Chapter 7: Neuro-fuzzy model construction, design and estimation * Chapter 8: A neural network approach for nearly optimal control of constrained nonlinear systems * Chapter 9: Reinforcement learning for online control and optimisation * Chapter 10: Reinforcement learning and multi-agent control within an internet environment * Chapter 11: Combined computational intelligence and analytical methods in fault diagnosis * Chapter 12: Application of intelligent control to autonomous search of parking place and parking of vehicles * Chapter 13: Applications of intelligent control in medicine
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