Dynamic Fuzzy Research Articles

Solving Randomly Generated Static and Dynamic Fuzzy Constraint Networks using Microevolutionary Hill-Climbing

ABSTRACTIn this article we combine the concept of microevolutionary hill-climbing search with the systematic search concept of arc revision to form a hybrid system that quickly finds solutions to static and dynamic Fuzzy Constraint Truth Optimization Problems (FCTOPs). Furthermore, we present the results of two experiments. In the first experiment, our microevolutionary hybrid system outperforms a modified version of a well known hill-climber, the Iterative Descent Method (IDM), on a test suite of 500 randomly generated static Fuzzy Constraint Networks.In the second experiment, we compare two methods for solving FCTOPs using a test suite of an additional 250 randomly generated FCTOPs. In the first method, all the constraints of a FCTOP are known by the hybrid system at run-time. We refer to this method as the static method for solving FCTOPs. In the second method, only half of the constraints of a FCTOP are known at run-time. Each time our hybrid system discovers a solution that satisfies all of the const...

Dynamic Fuzzy State-Feedback Controller and its Limitations

Fuzzy state-feedback controllers can be designed on the basis of linear Takagi-Sugeno fuzzy models when the premises of the IF- THEN rules do not involve the current value of the control variable u(k). The occurrence of the latter in some rule premises indeed would result in problems of logical inconsistency. This paper attempts to solve these problems through the introduction of dynamic state-feedback controllers. These can be obtained by solving linear matrix inequalities corresponding to a quadratic Lyapunov function used for stability analysis of the control loop, Eventually, limitations of such fuzzy controllers are discussed.

Dynamic programming approach to multiple objective control problem having deterministic or fuzzy goals

Multiple Objective Control problems (MOCP) are dynamic optimization problems of control systems that can be framed as multistage decision processes. Generalized Dynamic Programming or Multiple Objective Dynamic Programming is a useful technique to solve MOCP. Levary has first given the dynamic programming approach to solve a multiple objective optimization problem involving goal objectives. His formulation is valid if there is only a single decision variable in each stage. Generally, in control problems, such as Multiple Goal Control Problem (MGCP), a decision vector is involved in each stage. Therefore, in this paper Levary's formulation is extended to MGCP and is illustrated with an example. When the MOCP has fuzzy goals, it is formulated as Fuzzy Goal Control Problem (FGCP). A new formulation of dynamic programming approach to FGCP is proposed here as Dynamic Fuzzy Goal Control Problem (DFGCP) and the method of finding its solution is illustrated with an example.

On fuzzy multi-valued functions, part 4: Fuzzy economic models

An application to fuzzy economic systems of the fuzzy multi-valued functions theory elaborated by both of us in our previous papers is presented. First, stationary and dynamic fuzzy economic models are defined. Next the optimality of fuzzy trajectories of these models is introduced and investigated.