The Experts below are selected from a list of 569109 Experts worldwide ranked by ideXlab platform
H.o. Wang - One of the best experts on this subject based on the ideXlab platform.
-
FUZZ-IEEE - Switching fuzzy Model Construction based on optimal dividing planes
2009 IEEE International Conference on Fuzzy Systems, 2009Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents switching fuzzy Model Construction based on optimal dividing planes. In our previou papers, we proposed the switching fuzzy Model with arbitrary dividing planes. However, dividing planes have been selected by controller designers before constructing the switching fuzzy Model. Unfortunately, it is difficult to find suitable dividing planes for complicated nonlinear systems. Moreover, the switching fuzzy Models constructed from different dividing planes are different. Therefore, the Model Construction affects the stability analysis results. In this paper, we propose switching fuzzy Model Construction method using multi-dimensional sector nonlinearity concept based on optimal dividing planes. The linear consequent parts of the switching fuzzy Model based on optimal dividing planes are constructed from sectors which have minimum distance between a nonlinear system and sectors, and have richer information of the nonlinear system than the consequent parts of the ordinary Takagi-Sugeno fuzzy Model. Two examples are provided to illustrate the validity of this approach.
-
Piecewise Fuzzy Model Construction and Controller Design based on
2007Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents piecewise fuzzy Model con- struction divided by arbitrary affine hyper-planes and con- troller design based on the so-called piecewise Lyapunov function. In our previous papers, we proposed the switching fuzzy Model Construction method with arbitrary dividing hyper- planes. However, we assumed that the dividing planes contain the origin, that is, linear hyper-planes in the papers. In this paper, we propose piecewise fuzzy Model Construction method with arbitrary dividing hyper-planes which are permitted not to pass through the origin. We can obtain sectors which can cover nonlinear dynamics more tightly than ordinary method. By utilizing continuity matrices which are constructed from coefficients of dividing hyper-planes, we construct piecewise fuzzy Model which is continuous on boundaries of divided re- gions caused by dividing planes. Moreover, we derive controller design conditions based on the so-called piecewise Lyapunov function in terms of LMIs.
-
ACC - Piecewise Fuzzy Model Construction and Controller Design based on Piecewise Lyapunov Function
2007 American Control Conference, 2007Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents piecewise fuzzy Model Construction divided by arbitrary affine hyper-planes and controller design based on the so-called piecewise Lyapunov function. In our previous papers, we proposed the switching fuzzy Model Construction method with arbitrary dividing hyper- planes. However, we assumed that the dividing planes contain the origin, that is, linear hyper-planes in the papers. In this paper, we propose piecewise fuzzy Model Construction method with arbitrary dividing hyper-planes which are permitted not to pass through the origin. We can obtain sectors which can cover nonlinear dynamics more tightly than ordinary method. By utilizing continuity matrices which are constructed from coefficients of dividing hyper-planes, we construct piecewise fuzzy Model which is continuous on boundaries of divided regions caused by dividing planes. Moreover, we derive controller design conditions based on the so-called piecewise Lyapunov function in terms of LMIs.
-
Switching fuzzy Model Construction and controller design with arbitrary switching planes
2006 American Control Conference, 2006Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents switching fuzzy Model Construction with arbitrary switching planes and controller design based on the switching Lyapunov function. In our previous papers, we proposed the switching fuzzy Model with switching planes corresponding to quadrants. However, it is obvious that a switching fuzzy Model differs with respect to switching planes and the Model Construction affects the stability analysis results. In this paper, we propose switching fuzzy Model Construction with arbitrary switching planes and derive controller design conditions based on the switching Lyapunov function in terms of LMIs.
Hiroshi Ohtake - One of the best experts on this subject based on the ideXlab platform.
-
FUZZ-IEEE - Switching fuzzy Model Construction based on optimal dividing planes
2009 IEEE International Conference on Fuzzy Systems, 2009Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents switching fuzzy Model Construction based on optimal dividing planes. In our previou papers, we proposed the switching fuzzy Model with arbitrary dividing planes. However, dividing planes have been selected by controller designers before constructing the switching fuzzy Model. Unfortunately, it is difficult to find suitable dividing planes for complicated nonlinear systems. Moreover, the switching fuzzy Models constructed from different dividing planes are different. Therefore, the Model Construction affects the stability analysis results. In this paper, we propose switching fuzzy Model Construction method using multi-dimensional sector nonlinearity concept based on optimal dividing planes. The linear consequent parts of the switching fuzzy Model based on optimal dividing planes are constructed from sectors which have minimum distance between a nonlinear system and sectors, and have richer information of the nonlinear system than the consequent parts of the ordinary Takagi-Sugeno fuzzy Model. Two examples are provided to illustrate the validity of this approach.
-
Switching Fuzzy Model Construction and Controller Design for Dynamical Systems with Input Nonlinearity
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2008Co-Authors: Hiroshi Ohtake, Kazuo TanakaAbstract:Fuzzy Model-based control mainly deals with dynamical systems which affinely depend on control inputs. In this paper, dynamical systems which is permitted to have nonlinearity not only in the states, but also in the inputs is considered. Input nonlinearity makes a nonlinear system complicated and makes the number of fuzzy Model rules increase. Thus, switching fuzzy control approach is employed. Firstly, the switching fuzzy Model Construction with arbitrary linear dividing planes, which is an extension of the ordinary switching fuzzy Model Construction method with dividing planes corresponding to quadrants, is introduced. Secondly, by applying the switching fuzzy Model Construction method to the dynamical system with input nonlinearity, the switching fuzzy Model with membership functions which depend on control inputs is constructed. Finally, by utilizing the dynamic state feedback control approach, we show that membership functions which depend on control inputs can be calculated. Moreover, by employing augmented system approach, the switching fuzzy dynamic state feedback controller design conditions based on the switching Lyapunov function are derived in terms of linear matrix inequalities. A design example illustrates the utility of this approach.
-
Piecewise Fuzzy Model Construction and Controller Design based on
2007Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents piecewise fuzzy Model con- struction divided by arbitrary affine hyper-planes and con- troller design based on the so-called piecewise Lyapunov function. In our previous papers, we proposed the switching fuzzy Model Construction method with arbitrary dividing hyper- planes. However, we assumed that the dividing planes contain the origin, that is, linear hyper-planes in the papers. In this paper, we propose piecewise fuzzy Model Construction method with arbitrary dividing hyper-planes which are permitted not to pass through the origin. We can obtain sectors which can cover nonlinear dynamics more tightly than ordinary method. By utilizing continuity matrices which are constructed from coefficients of dividing hyper-planes, we construct piecewise fuzzy Model which is continuous on boundaries of divided re- gions caused by dividing planes. Moreover, we derive controller design conditions based on the so-called piecewise Lyapunov function in terms of LMIs.
-
ACC - Piecewise Fuzzy Model Construction and Controller Design based on Piecewise Lyapunov Function
2007 American Control Conference, 2007Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents piecewise fuzzy Model Construction divided by arbitrary affine hyper-planes and controller design based on the so-called piecewise Lyapunov function. In our previous papers, we proposed the switching fuzzy Model Construction method with arbitrary dividing hyper- planes. However, we assumed that the dividing planes contain the origin, that is, linear hyper-planes in the papers. In this paper, we propose piecewise fuzzy Model Construction method with arbitrary dividing hyper-planes which are permitted not to pass through the origin. We can obtain sectors which can cover nonlinear dynamics more tightly than ordinary method. By utilizing continuity matrices which are constructed from coefficients of dividing hyper-planes, we construct piecewise fuzzy Model which is continuous on boundaries of divided regions caused by dividing planes. Moreover, we derive controller design conditions based on the so-called piecewise Lyapunov function in terms of LMIs.
-
Switching fuzzy Model Construction and controller design with arbitrary switching planes
2006 American Control Conference, 2006Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents switching fuzzy Model Construction with arbitrary switching planes and controller design based on the switching Lyapunov function. In our previous papers, we proposed the switching fuzzy Model with switching planes corresponding to quadrants. However, it is obvious that a switching fuzzy Model differs with respect to switching planes and the Model Construction affects the stability analysis results. In this paper, we propose switching fuzzy Model Construction with arbitrary switching planes and derive controller design conditions based on the switching Lyapunov function in terms of LMIs.
Kazuo Tanaka - One of the best experts on this subject based on the ideXlab platform.
-
A practical design approach to automatic Model Construction and controller design for F16 aircraft
2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 2013Co-Authors: Daisuke Ogura, Ying-jen Chen, Motoyasu Tanaka, Kazuo TanakaAbstract:This paper presents a practical design approach to automatic Model Construction and controller design for the F16 aircraft. The automatic Model Construction and controller design are realized using the framework of a Takagi-Sugeno (T-S) fuzzy Modeling and control. Any trimmed equilibriums in addition to the complicated dynamics describing in an F16 aircraft simulator are considered and automatically converted to T-S fuzzy Models around any trimmed equilibriums. A linear matrix inequality based design is carried out to automatically obtain stable nonlinear T-S fuzzy controllers according to the considered trimmed equilibriums. Control performance of the designed controllers are tested for the original dynamics (not T-S fuzzy Models). The testing results demonstrate the utility of the practical design approach to automatic Model Construction and controller design.
-
FUZZ-IEEE - Switching fuzzy Model Construction based on optimal dividing planes
2009 IEEE International Conference on Fuzzy Systems, 2009Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents switching fuzzy Model Construction based on optimal dividing planes. In our previou papers, we proposed the switching fuzzy Model with arbitrary dividing planes. However, dividing planes have been selected by controller designers before constructing the switching fuzzy Model. Unfortunately, it is difficult to find suitable dividing planes for complicated nonlinear systems. Moreover, the switching fuzzy Models constructed from different dividing planes are different. Therefore, the Model Construction affects the stability analysis results. In this paper, we propose switching fuzzy Model Construction method using multi-dimensional sector nonlinearity concept based on optimal dividing planes. The linear consequent parts of the switching fuzzy Model based on optimal dividing planes are constructed from sectors which have minimum distance between a nonlinear system and sectors, and have richer information of the nonlinear system than the consequent parts of the ordinary Takagi-Sugeno fuzzy Model. Two examples are provided to illustrate the validity of this approach.
-
Switching Fuzzy Model Construction and Controller Design for Dynamical Systems with Input Nonlinearity
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2008Co-Authors: Hiroshi Ohtake, Kazuo TanakaAbstract:Fuzzy Model-based control mainly deals with dynamical systems which affinely depend on control inputs. In this paper, dynamical systems which is permitted to have nonlinearity not only in the states, but also in the inputs is considered. Input nonlinearity makes a nonlinear system complicated and makes the number of fuzzy Model rules increase. Thus, switching fuzzy control approach is employed. Firstly, the switching fuzzy Model Construction with arbitrary linear dividing planes, which is an extension of the ordinary switching fuzzy Model Construction method with dividing planes corresponding to quadrants, is introduced. Secondly, by applying the switching fuzzy Model Construction method to the dynamical system with input nonlinearity, the switching fuzzy Model with membership functions which depend on control inputs is constructed. Finally, by utilizing the dynamic state feedback control approach, we show that membership functions which depend on control inputs can be calculated. Moreover, by employing augmented system approach, the switching fuzzy dynamic state feedback controller design conditions based on the switching Lyapunov function are derived in terms of linear matrix inequalities. A design example illustrates the utility of this approach.
-
Piecewise Fuzzy Model Construction and Controller Design based on
2007Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents piecewise fuzzy Model con- struction divided by arbitrary affine hyper-planes and con- troller design based on the so-called piecewise Lyapunov function. In our previous papers, we proposed the switching fuzzy Model Construction method with arbitrary dividing hyper- planes. However, we assumed that the dividing planes contain the origin, that is, linear hyper-planes in the papers. In this paper, we propose piecewise fuzzy Model Construction method with arbitrary dividing hyper-planes which are permitted not to pass through the origin. We can obtain sectors which can cover nonlinear dynamics more tightly than ordinary method. By utilizing continuity matrices which are constructed from coefficients of dividing hyper-planes, we construct piecewise fuzzy Model which is continuous on boundaries of divided re- gions caused by dividing planes. Moreover, we derive controller design conditions based on the so-called piecewise Lyapunov function in terms of LMIs.
-
ACC - Piecewise Fuzzy Model Construction and Controller Design based on Piecewise Lyapunov Function
2007 American Control Conference, 2007Co-Authors: Hiroshi Ohtake, Kazuo Tanaka, H.o. WangAbstract:This paper presents piecewise fuzzy Model Construction divided by arbitrary affine hyper-planes and controller design based on the so-called piecewise Lyapunov function. In our previous papers, we proposed the switching fuzzy Model Construction method with arbitrary dividing hyper- planes. However, we assumed that the dividing planes contain the origin, that is, linear hyper-planes in the papers. In this paper, we propose piecewise fuzzy Model Construction method with arbitrary dividing hyper-planes which are permitted not to pass through the origin. We can obtain sectors which can cover nonlinear dynamics more tightly than ordinary method. By utilizing continuity matrices which are constructed from coefficients of dividing hyper-planes, we construct piecewise fuzzy Model which is continuous on boundaries of divided regions caused by dividing planes. Moreover, we derive controller design conditions based on the so-called piecewise Lyapunov function in terms of LMIs.
Ramayya Krishnan - One of the best experts on this subject based on the ideXlab platform.
-
Computer-aided Model Construction
Decision Support Systems, 1993Co-Authors: Hemant K. Bhargava, Ramayya KrishnanAbstract:Abstract We examine ways in which the Construction of mathematical Models may be supported, and review several approaches for computer-aided Model Construction. The Construction of complex Models can be a challenging task even for expert Modelers. The aim of computer-aided Model Construction systems is to simplify this task. We view Model Construction as a state transformation process, and suggest that the process can be facilitated by supporting the creation of certain representations or by transforming an existing representation to another. For example, declarative Modeling languages facilitate the specification of an executable statement of the mathematical Model, while knowledge-based Model Construction systems assist the creation of the mathematical formulation. Knowledge-based Model Construction systems are the focus of our review of existing systems. To facilitate a comparison of several such systems, we characterize them in terms of their cognitive bases, the representations developed in them, and the methods for transformation of these representations.
-
PDM: A knowledge-based tool for Model Construction
Decision Support Systems, 1991Co-Authors: Ramayya KrishnanAbstract:Abstract This paper describes PDM, a knowledge-based tool designed to help non-expert users construct Linear Programming (LP) Models of Production, Distribution and Inventory (PDI) planning problems. PDM interactively aids users in defining a qualitative Model of their planning problem, and employs it to generate problem-specific inferences and as input to a Model building component that mechanically constructs the algebraic schema of the appropriate LP Model. Interesting features of PDM include the application of domain knowledge to guide user interaction, the use of syntactic knowledge of the problem representation language to effect Model revision, and in the use of a small set of primitive Modeling rules in Model Construction.
-
PDM: a knowledge-based tool for Model Construction
[1989] Proceedings of the Twenty-Second Annual Hawaii International Conference on System Sciences. Volume III: Decision Support and Knowledge Based Sy, 1Co-Authors: Ramayya KrishnanAbstract:A description is given of PDM, a knowledge-based tool designed to help nonexpert users construct linear programming (LP) Models of production, distribution, and inventory (PDI) planning problems. PDM interactively aids users in defining a logic Model of their planning problem and uses it to generate problem-specific inferences and as input to a Model building component that mechanically constructs the algebraic schema of the appropriate LP Model. Interesting features of PDM include the application of domain knowledge to guide user interaction, the use of syntactic knowledge of the problem representation language to effect Model revision, and the use of a small set of primitive Modeling rules in Model Construction. >
Soe-tsyr Yuan - One of the best experts on this subject based on the ideXlab platform.
-
Knowledge-Based Decision Model Construction for Hierarchical Diagnosis: A Preliminary Report
arXiv: Artificial Intelligence, 2013Co-Authors: Soe-tsyr YuanAbstract:Numerous methods for probabilistic reasoning in large, complex belief or decision networks are currently being developed. There has been little research on automating the dynamic, incremental Construction of decision Models. A uniform value-driven method of decision Model Construction is proposed for the hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is formulated as a stochastic process and Modeled using influence diagrams. Given observations, this method creates decision Models in order to obtain the best actions sequentially for locating and repairing a fault at minimum cost. This method construct decision Models incrementally, interleaving probe actions with Model Construction and evaluation. The method treats meta-level and baselevel tasks uniformly. That is, the method takes a decision-theoretic look at the control of search in causal pathways and structural hierarchies.
-
UAI - Knowledge-based decision Model Construction for hierarchical diagnosis: a preliminary report
Uncertainty in Artificial Intelligence, 1993Co-Authors: Soe-tsyr YuanAbstract:Numerous methods for probabilistic reasoning in large, complex belief or decision networks are currently being developed. There has been little research on automating the dynamic, incremental Construction of decision Models. A uniform value-driven method of decision Model Construction is proposed for the hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is formulated as a stochastic process and Modeled using influence diagrams. Given observations, this method creates decision Models in order to obtain the best actions sequentially for locating and repairing a fault at minimum cost. This method construct decision Models incrementally, interleaving probe actions with Model Construction and evaluation. The method treats meta-level and base-level tasks uniformly. That is, the method takes a decision-theoretic look at the control of search in causal pathways and structural hierarchies.
