Ebook: Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems

The objects of modelling and control change due to dynamical characteristics, fault development or simply ageing. There is a need to up-date models inheriting useful structure and parameter information. The book gives an original solution to this problem with a number of examples. It treats an original approach to on-line adaptation of rule-based models and systems described by such models. It combines the benefits of fuzzy rule-based models suitable for the description of highly complex systems with the original recursive, non iterative technique of model evolution without necessarily using genetic algorithms, thus avoiding computational burden making possible real-time industrial applications. Potential applications range from autonomous systems, on-line fault detection and diagnosis, performance analysis to evolving (self-learning) intelligent decision support systems.

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The objects of modelling and control change due to dynamical characteristics, fault development or simply ageing. There is a need to up-date models inheriting useful structure and parameter information. The book gives an original solution to this problem with a number of examples. It treats an original approach to on-line adaptation of rule-based models and systems described by such models. It combines the benefits of fuzzy rule-based models suitable for the description of highly complex systems with the original recursive, non iterative technique of model evolution without necessarily using genetic algorithms, thus avoiding computational burden making possible real-time industrial applications. Potential applications range from autonomous systems, on-line fault detection and diagnosis, performance analysis to evolving (self-learning) intelligent decision support systems.

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The objects of modelling and control change due to dynamical characteristics, fault development or simply ageing. There is a need to up-date models inheriting useful structure and parameter information. The book gives an original solution to this problem with a number of examples. It treats an original approach to on-line adaptation of rule-based models and systems described by such models. It combines the benefits of fuzzy rule-based models suitable for the description of highly complex systems with the original recursive, non iterative technique of model evolution without necessarily using genetic algorithms, thus avoiding computational burden making possible real-time industrial applications. Potential applications range from autonomous systems, on-line fault detection and diagnosis, performance analysis to evolving (self-learning) intelligent decision support systems.
Content:
Front Matter....Pages i-xiii
Introduction....Pages 1-9
Front Matter....Pages 11-11
Conventional Models....Pages 13-24
Flexible Models....Pages 25-42
Front Matter....Pages 43-46
Non-Linear Approach to (Off-line) Identification of Flexible Models....Pages 47-65
Quasi-Linear Approach to FRB Models (Off-Line) Identification....Pages 67-77
Intelligent and Smart Adaptive Systems....Pages 79-85
On-Line Identification of Flexible TSK-Type Models....Pages 87-109
Front Matter....Pages 111-113
Modelling Indoor Climate Control Systems....Pages 115-167
On-Line Modelling of Fermentation Processes....Pages 169-179
Intelligent Risk Assesment....Pages 181-192
Conclusions....Pages 193-197
Back Matter....Pages 199-213

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The objects of modelling and control change due to dynamical characteristics, fault development or simply ageing. There is a need to up-date models inheriting useful structure and parameter information. The book gives an original solution to this problem with a number of examples. It treats an original approach to on-line adaptation of rule-based models and systems described by such models. It combines the benefits of fuzzy rule-based models suitable for the description of highly complex systems with the original recursive, non iterative technique of model evolution without necessarily using genetic algorithms, thus avoiding computational burden making possible real-time industrial applications. Potential applications range from autonomous systems, on-line fault detection and diagnosis, performance analysis to evolving (self-learning) intelligent decision support systems.
Content:
Front Matter....Pages i-xiii
Introduction....Pages 1-9
Front Matter....Pages 11-11
Conventional Models....Pages 13-24
Flexible Models....Pages 25-42
Front Matter....Pages 43-46
Non-Linear Approach to (Off-line) Identification of Flexible Models....Pages 47-65
Quasi-Linear Approach to FRB Models (Off-Line) Identification....Pages 67-77
Intelligent and Smart Adaptive Systems....Pages 79-85
On-Line Identification of Flexible TSK-Type Models....Pages 87-109
Front Matter....Pages 111-113
Modelling Indoor Climate Control Systems....Pages 115-167
On-Line Modelling of Fermentation Processes....Pages 169-179
Intelligent Risk Assesment....Pages 181-192
Conclusions....Pages 193-197
Back Matter....Pages 199-213
....