We propose a genetic algorithm for mutually connected neural networks to obtain a higher capacity of associative memory. In Hop eld network as an associative memory system, the memory capacity is at most 15% of the number of neurons. Here we applied our method to the Hop eld network, and obtained the capacity of 33%. We conjectured that this is due to both asymmetry and sparseness of the connec...

|There have been a lot of researches which apply evolutionary techniques to layered neural networks. However, their applications to Hop eld neural networks remain few so far. We have been applying Genetic Algorithms to fully connected associative memory model of Hop eld, and reported elsewhere that the network can store some number of patterns only by evolving weight matrices with a simple Gene...

The consequences of two techniques for symmetrically diluting the weights of the standard Hopfield architecture associative memory model, trained using a non-Hebbian learning rule, are examined. This paper reports experimental investigations into the effect of dilution on factors such as: pattern stability and attractor performance. It is concluded that these networks maintain a reasonable leve...

It has been found that the performance of an associative memory model trained with the perceptron learning rule can be improved by increasing the learning threshold. When the learning threshold increases, the range of possible values of the update threshold becomes wider and the network may perform differently with different choices of this parameter. This paper investigates the effect of varyi...

We discuss eeects of realistically dilute connectivity on a neural network previously proposed as a model of hippocampal associative memory. Several criteria for setting neuronal activation thresholds are studied. Even with optimal thresholding, dilution induces a substantial information loss in stored patterns compared to presented patterns. Consequently, a stricter constraint than previous on...

Globally coupled map (GCM) model can evolve through chaotic searching into several stable periodic orbits under properly controlled parameters. This can be exploited in information processing such as associative memory and optimization. In this paper, we propose a novel covariance learning rule for multivalue patterns and apply it in memorization of gray-scale images based on modified GCM model...

In recent years dynamic optimization problems have attracted a growing interest from the community of genetic algorithms with several approaches developed to address these problems, of which the memory scheme is a major one. In this paper an associative memory scheme is proposed for genetic algorithms to enhance their performance in dynamic environments. In this memory scheme, the environmental...

Contrary to the traditional base 2 binary number system, used in today’s computers, in which a complex number is represented by two separate binary entities, one for the real part and one for the imaginary part, Complex Binary Number System (CBNS), a binary number system with base (−1+j), is used to represent a given complex number in single binary string format. In this paper, CBNS is reviewed...

Embedded content addressable memories (CAMs) are important components in many system chips. In this paper two efficient March-like test algorithms are proposed. In addition to typical RAM faults, they also cover CAMspecific comparison faults. The first algorithm requires 9N Read/Write operations and 2(N + W ) Compare operations to cover comparison and RAM faults (but does not fully cover the in...