This paper presents an improved technique for accelerating the process of doing logic programming in discrete Hopfield neural network by integrating fuzzy logic and modifying activation function. Generally Hopfield networks are suitable for solving combinatorial optimization problems and pattern recognition problems. However Hopfield neural networks also face some limitations; one of the major ...

Developing technologies have made significant progress towards linking the brain with brain-machine interfaces (BMIs) which have the potential to aid damaged brains to perform their original motor and cognitive functions. We consider the viability of such devices for mitigating the deleterious effects of memory loss that is induced by neurodegenerative diseases and/or traumatic brain injury (TB...

We consider the Hopfield model with n neurons and an increasing number p = p(n) of randomly chosen patterns. Under the condition (p3 log p)/n → 0, we prove for every fixed choice of overlap parameters a central limit theorem as n → ∞, which holds for almost all realizations of the random patterns. In the special case where the temperature is above the critical one and there is no external magne...

It is shown that conventional computers can be exponentiallx faster than planar Hopfield networks: although there are planar Hopfield networks that take exponential time to converge, a stable state of an arbitrary planar Hopfield network can be found by a conventional computer in polynomial time. The theory of 'P.cS-completeness gives strong evidence that such a separation is unlikely for nonpl...

In this paper, a new Hopfield-model net called Compensated Fuzzy Hopfield Neural Network (CFHNN) is proposed for vector quantization in image compression. In CFHNN, the compensated fuzzy c-means algorithm, modified from penalized fuzzy cmeans, is embedded into Hopfield neural network so that the parallel implementation for codebook design is feasible. The vector quantization can be cast as an o...

In this report we investigate the storage capacity of an abstract generic attractor neural network model of the mammalian cortex. This model network has a diluted connection matrix and a fixed activity level that is independent of network size. We develop an analytical model of the storage capacity for this type of networks when they are trained with both the Willshaw and Hopfield learning-rule...

Compounds are very common in many kinds of language. Most of the research in this field is from the view of morphology, while artificial neural network is seldom concerned. Based on Hopfield model, we create a novel neural network to simulate the recognition process of compounds in English and Chinese. Our model is composed of two layers: abstraction layer and recognition layer. The first layer...

Multi-modal optimisation problems are characterised by the presence of either local sub-optimal points or a number of equally optimal points. These local optima can be considered as point attractors for hill climbing search algorithms. It is desirable to be able to model them either to avoid mistaking a local optimum for a global one or to allow the discovery of multiple equally optimal solutio...

The signal-to-noise analysis of the Little-Hopfield model revisited Abstract Using the generating functional analysis an exact recursion relation is derived for the time evolution of the effective local field of the fully connected Little-Hopfield model. It is shown that, by leaving out the feedback correlations arising from earlier times in this effective dynamics, one precisely finds the recu...

A ternary/binary data coding algorithm and conditions under which Hopfield networks implement optimal convolutional and Hamming decoding algorithms has been described. Using the coding/decoding approach (an optimal Binary Signal Detection Theory, BSDT) introduced a Neural Network Assembly Memory Model (NNAMM) is built. The model provides optimal (the best) basic memory performance and demands t...