This paper is concerned with inertial bidirectional associative memory neural networks with mixed delays and impulsive effects. New and practical conditions are given to study the existence, uniqueness, and global exponential stability of anti-periodic solutions for the suggested system. We use differential inequality techniques to prove our main results. Finally, we give an illustrative example to demonstrate the effectiveness of our new results.

This paper focuses on the finite-time synchronization for a class of fuzzy inertial cellular neural networks (FICNNs) with time-varying delays. First, by constructing a proper variable substitution, the original FICNNs with time-varying delays can be rewritten as first-order differential system. Second, based on Lyapunov functionals, we derive some new and sufficient conditions of finite-time synchronization for the addressed system. We illustrate the effectiveness of the approach through a few examples.

In this paper, a class of neutral type fuzzy neural networks with non-operator based neutral functional differential equations is analysed. Using the Banach's fixed point principle, some sufficient conditions for the existence, the uniqueness and the global exponential stability of the almost automorphic solutions are obtained. Two examples are given to illustrate the theoretical results.

In this paper, we provide an improvement of the circuit implementation of a one-layer recurrent neural network for support vector machine learning in pattern classification and regression. Our goal is to reduce the complexity of this architecture. Numerical example with graphical illustration is given to illuminate our main results.

We propose an evolutionary method for the design of Beta fuzzy systems (BFS). Classical training algorithms start with a predetermined number of fuzzy rules for fuzzy systems. Generally speaking, the fuzzy system created is either insufficient or over-complicated. This paper describes a hierarchical genetic learning model of the BFS. In order to examine the performance of the proposed algorithm, it is used for the identification of an induction machine fuzzy plant model. The results obtained have been encouraging.

The aim of the work was the full design of a Beta fuzzy system for the modeling of nonlinear processes. We propose a hierarchical genetic algorithm with two nodes connected together. Each node is a real coded genetic algorithm allowing the migration of individuals between each other. These two algorithms are based on a Pittsburgh-style approach where each chromosome encodes a set of knowledge bases. Our main contribution results in the genetic representation wherein each individual is coded as a two-dimension matrix, the number of lines is equal to the number of input variables whereas four columns of the matrix represent one fuzzy rule. One distinguishing feature of this approach is that it gives a standard solution to building a fuzzy logic system and neural networks.