Sliding mode control is introduced into classical model free fuzzy logic control for discrete time nonlinear systems with uncertainty to the design of a novel fuzzy sliding mode control to meet the requirement of necessary and sufficient reaching conditions of sliding mode control. The simulation results show that the proposed controller outperforms the original fuzzy sliding mode controller an...

| This paper investigates the possibilities for applications of genetic algorithms to tuning and optimizing fuzzy controllers, or even to generate fuzzy controllers automatically. There are various ad{hoc approaches to use genetic algorithms for the design of fuzzy controllers, which already indicated good results. However, there is a need for systematic techniques that take the properties of f...

In this paper, the ability of Immune Algorithm (IA) is investigated for VAr planning with the Static Var Compensator (SVC) in a large-scale power system. To enhance voltage stability, the planning problem is formulated as a multi-objective optimization problem for maximizing fuzzy performance indices. The multi-objective VAr planning problem is solved by the fuzzy IA and the results are compare...

In a fuzzy topology on a set X, the limit of a prefilter (i.e. a filter in the lattice [0,i] X) is calculated from the fuzzy closure. In this way convergence is derived from a fuzzy topology. In our paper we start with any rule "lira" which to any prefilter on X assigns, a function lira E [0,i] X. We give necessary and sufficient conditions for the function lim in order that it can be derived f...

The defects of BP neural network, such as low convergence speed, falling into local minimum easily, bad generalization ability, can depress the calculation accuracy of BP neural network and damage its practical effect. So the research of improving BP neural network has great theoretical and practical significance. The paper advances a new fuzzy neural network algorithm to overcome the defects o...

This paper presents a novel improved fuzzy particle swarm optimization (IFPSO) algorithm to the intelligent identification and control of a dynamic system. The proposed algorithm estimates optimally the parameters of system and controller by minimizing the mean of squared errors. The particle swarm optimization is enhanced intelligently by using a fuzzy inertia weight to rationally balance the ...

Applications of neural network were widely used in construct project cost estimate. Aim at handling weakness of poor convergence and insufficient forecast, an improved fuzzy neural network method based on SOFM (self-organizing feature map) was proposed to replace the fashionable T-S fuzzy neural network. The method illustrated how to apply SOFM to improve the fault such as poor convergence and ...

This paper proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN). The RFNN...

Introduction § 0. Preliminaries: fuzzy sets 125 § 1. Fuzzy topological spaces: the basic categories of fuzzy topology 127 § 2. Fundamental interrelations between the category Top of topological 135 spaces and the categories of fuzzy topology § 3. Local structure of fuzzy topological spaces 138 § 4. Convergence structures in fuzzy spaces 140 § 5. Separation in fuzzy spaces 143 § 6. Normality and...

This paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. The uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. The contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of inducti...