In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associati...

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 ...

A design procedure of neural associative memories to be used for robot vision systems is developed, which fits in the capabilities both of discrete-time cellular neural networks (DTCNNs) and fuzzy logic. The choice of this kind of neural networks is motivated by their architecture, suitable for storing images, and their locally connected structure, which is effective for the hardware implementa...

The neural activity of the human brain is dominated by self-sustained activities. External sensory stimuli influence this autonomous activity but they do not drive the brain directly. Most standard artificial neural network models are however input driven and do not show spontaneous activities. It constitutes a challenge to develop organizational principles for controlled, self-sustained activi...

s. The model of associative memory is proposed in this paper based on concept of association between images and associative search. This model may be used for connect concept of inference with associative processing in neural networks.

In this paper, we present FPGA recurrent neural network systems with learning capability using the simultaneous perturbation learning rule. In the neural network systems, outputs and internal values are represented by pulse train. That is, analog recurrent neural networks with pulse frequency representation are considered. The pulse density representation and the simultaneous perturbation enabl...

Probabilistic inference networks capture the stochastic relation between variables by ‘directed’ probabilistic rules corresponding to conditional probabilities, e.g. p(Ak|Ai∧Aj). Associative neural networks – like Boltzmann machine networks – yield a joint distribution, which is a special case of the distribution generated by inference networks. In this paper conventional associative neural net...

Most models of neural associative memory have used networks with broad connectivity. However, from both a neurobiological viewpoint and an implementation perspective, it is logical to minimize the length of inter-neural connections and consider networks whose connectivity is predominantly local. The \small-world networks" model described recently by Watts and Strogatz provides an interesting ap...

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