Search

Abstract: Recently, some continuous-time recurrent neural networks have been proposed for associative memories based on optimizing linear or quadratic programming problems. In this paper, a simple and efficient neural network with a single recurrent unit is proposed for realizing associative memories. Compared with the existing neural networks for associative memories, the main advantage of the proposed model is that it has only one recurrent unit, which lowers the model complexity by the greatest extent. In the proposed neural network, each prototype pattern is stored in the connection weights between the input and hidden layers. In addition, the advanced performance of the proposed network is demonstrated by means of simulations of three numerical examples. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the stability analysis of neutral type neural networks with discrete and distributed delays. Some improved delay-dependent stability results are established by using a delay partitioning approach for the networks. By employing a new type of Lyapunov–Krasovskii functionals, new delay-dependent stability criteria are derived. All the criteria are expressed in terms of linear matrix inequalities (LMIs), which can be solved efficiently by using standard convex optimization algorithms. Finally, numerical examples are given to illustrate the less conservatism of the proposed method. [Copyright &y& Elsevier]

Abstract: The classification ability of a single-layer perceptron could be improved by considering some enhanced features. In particular, this form of neural networks is called a functional-link net. In the output neuron''s activation function, such as the sigmoid function, an inner product of a connection weight vector with an input vector is computed. However, since the input features are not independent of each other for the enhanced pattern, an assumption of the additivity is not reasonable. This paper employs a non-additive technique, namely the fuzzy integral, to aggregate performance values for an input pattern by interpreting each of the connection weights as a fuzzy measure of the corresponding feature. A learning algorithm with the genetic algorithm is then designed to automatically find connection weights. The sample data for bankruptcy analysis obtained from Moody''s Industrial Manuals is considered to examine the classification ability of the proposed method. The results demonstrate that the proposed method performs well in comparison with traditional functional-link net and multivariate techniques. [Copyright &y& Elsevier]