fuzzy model identification control (33 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware -This book presents a research work towards the Identification and Control of Non-Linear Systems based on Fuzzy Models approach. A TS fuzzy model has been implemented successfully to a known benchmark problem of the identification of non-linear plant data. FCM Clustering based approach has been used for the classification of input ¿output data points. After clustering gradient descent method is used for the learning of parameters. It has also been implemented on a real data problem which is a model of an operator¿s control of a chemical plant and the accuracy was comparable to the results reported in the literature. The entire system has been modeled using MATLAB 7.0/Simulink toolbox.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec tive use of heterogenous information in the form of numerical data, qualita tive knowledge and first-principle models. By exploiting the mathematical properties of the proposed model structures, such as invertibility and local linearity, new control algorithms will be presented.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec tive use of heterogenous information in the form of numerical data, qualita tive knowledge and first-principle models. By exploiting the mathematical properties of the proposed model structures, such as invertibility and local linearity, new control algorithms will be presented.

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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gupta SunilMr.Sunil Gupta is working as a Reader in Maharaja Surajmal Institute of Technology,(GGSIP University), Janak Puri, New Delhi. He is pursuing Ph.D. degree in Electrical Engineering at Jamia Millia Islamia (A Central Univers.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a research work towards the Identification and Control of Non-Linear Systems based on Fuzzy Models approach. A TS fuzzy model has been implemented successfully to a known benchmark problem of the identification of non-linear plant data. FCM Clustering based approach has been used for the classification of input -output data points. After clustering gradient descent method is used for the learning of parameters. It has also been implemented on a real data problem which is a model of an operator's control of a chemical plant and the accuracy was comparable to the results reported in the literature. The entire system has been modeled using MATLAB 7.0/Simulink toolbox.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a research work towards the Identification and Control of Non-Linear Systems based on Fuzzy Models approach. A TS fuzzy model has been implemented successfully to a known benchmark problem of the identification of non-linear plant data. FCM Clustering based approach has been used for the classification of input -output data points. After clustering gradient descent method is used for the learning of parameters. It has also been implemented on a real data problem which is a model of an operator's control of a chemical plant and the accuracy was comparable to the results reported in the literature. The entire system has been modeled using MATLAB 7.0/Simulink toolbox. 64 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files .

Gebunden. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files .

Buch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec tive use of heterogenous information in the form of numerical data, qualita tive knowledge and first-principle models. By exploiting the mathematical properties of the proposed model structures, such as invertibility and local linearity, new control algorithms will be presented.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 288 pp. Englisch.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec tive use of heterogenous information in the form of numerical data, qualita tive knowledge and first-principle models. By exploiting the mathematical properties of the proposed model structures, such as invertibility and local linearity, new control algorithms will be presented.Springer Basel AG in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 288 pp. Englisch.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files create a computational platform for exploration of the concepts and algorithms. 288 pp. Englisch.