Intelligent Control Systems Using Computational Intelligence TechniquesA.E. Ruano 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 Systems using Computational Intellingence Techniques details the application of these tools to the field of control systems. Each chapter gives and overview of current approaches in the topic covered, with a set of the most important references in the field, and then details the author's approach, examining both the theory and practical applications. |
Contents
1 An overview of nonlinear identification and control with fuzzy systems | 1 |
2 An overview of nonlinear identification and control with neural networks | 37 |
3 Multiobjective evolutionary computing solutions for control and system identification | 89 |
4 Adaptive local linear modelling and control of nonlinear dynamical systems | 119 |
5 Nonlinear system identification with local linear neurofuzzy models | 153 |
6 Gaussian process approaches to nonlinear modelling for control | 177 |
7 Neurofuzzy model construction design and estimation | 219 |
8 A neural network approach for nearly optimal control of constrained nonlinear systems | 253 |
9 Reinforcement learning for online control and optimisation | 293 |
10 Reinforcement learning and multiagent control within an internet environment | 327 |
11 Combined computational intelligence and analytical methods in fault diagnosis | 349 |
12 Application of intelligent control to autonomous search of parking place and parking of vehicles | 393 |
13 Applications of intelligent control in medicine | 415 |
| 447 | |
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Intelligent Control Systems Using Computational Intelligence Techniques A.E. Ruano Limited preview - 2005 |
Common terms and phrases
Action Network ADAC adaptive applications approximation artificial neural networks B-spline basis function behaviour closed-loop complexity computational constraints control law control systems controller design convergence Critic Network data set defined denotes distribution dynamic systems Engineering equation error evolutionary algorithms fault detection fault diagnosis feedback filter fuzzy logic fuzzy model fuzzy rule fuzzy sets fuzzy systems Gaussian process model Genetic algorithms global hyperparameters IEEE Transactions input space input-output intelligent inverse iteration least-squares linear models linearisation matrix measure membership functions methods minimise model structure multi-objective neural networks neuro-fuzzy neuro-fuzzy model neurons nonlinear systems obtained operating point optimal control optimisation orthogonal parameter estimation partition performance predictive control problem Proceedings Reference regression regularisation reinforcement learning residual robust Section selection shown in Figure signal simulation solution state-space submodel subspace system identification techniques temperature training data Transactions on Neural utilised validity functions variables variance vehicle weights