In this paper, we propose a simple and efficient method to construct an accurate fuzzy classification system. In order to optimize the generalization accuracy, we use ruleweight as a simple mechanism to tune the classifier and propose a new learning method to iteratively adjust the weight of fuzzy rules. The rule-weights in the proposed method are derived by solving the minimization problem thr...

In this work we study the asymptotic behavior of fuzzy solutions obtained by using Zadeh’s extension at the deterministic solutions of initial value problems. We obtain some result regarding the existence of fuzzy equilibrium points that generalize the already known results. As show earlier, the membership function of the fuzzy equilibrium points may be obtained in a relatively simple way. Also...

in this paper, we study an equivalence relation on the set of fuzzysubgroups of an arbitrary group g and give four equivalent conditions each ofwhich characterizes this relation. we demonstrate that with this equivalencerelation each equivalence class constitutes a lattice under the ordering of fuzzy setinclusion. moreover, we study the behavior of these equivalence classes under theaction of a...

This paper focuses on the robustness problem of full implication triple implication inference method for fuzzy reasoning. First of all, based on strong regular implication, the weighted logic metric for measuring distance between two fuzzy sets is proposed. Besides, under this metric, some robustness results of the triple implication method are obtained, which demonstrates that the triple impli...

In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule-based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results...

This paper presents an evolutionary learning algorithm to facilitate the design of fuzzy controllers for mobile robots. It discusses the concepts, feasibility, bene ts and limitations of current evolutionary techniques for fuzzy rule discovery and tuning. We propose an evolution strategy that optimizes the gain factors in the conclusion part of TakagiSugeno-Kang type fuzzy rules. We describe tw...

In this paper, a new method of predictive control is presented. In this approach, a well-known method of predictive functional control is combined with fuzzy model of the process. The prediction is based on fuzzy model given in the form of Takagi–Sugeno (T–S) type. The proposed fuzzy predictive control has been evaluated by implementation on heat-exchanger plant, which exhibits a strong nonline...

The paper offers evolving Takagi–Sugeno (T–S) fuzzy models for a nonlinear benchmark represented by the pendulum-crane system and focused on the dynamics of pendulum angular position behavior. The rule bases and parameters of T–S fuzzy models are continuously evolved by an online identification algorithm in terms of computing the potentials of new data points. Accepting the pendulum angle as mo...

This paper presents an outlook on the perspectives of applying fuzzy logic techniques in traffic and transportation systems analysis and control. To this end, the theoretical and methodological principles of the fuzzy logic approach are outlined. An overview is given of the fuzzy logic applications in the transportation and traffic-engineering field, especially in the areas of estimation and pr...

This paper describes a messy genetic algorithm for the automatic design of fuzzy logic controllers. The method is applied to adapt a wall following behavior behavior of a mobile robot.