Several neural network paradigms are discussed and their application to the recognition of handwritten digits is considered. In particular, linear auto-associative systems, threshold logic networks, backward error propagation models, Hopfield networks, and Boltzmann machines are considered. An explanation of each technique is presented and its application to dipt recognition is discussed. The t...

In this article, we propose a robust person identification algorithm in video that is able to detect and identify faces. The method we have developed combines approaches for detection and recognition using an efficient correlation technique and auto associative neural network. The experiments confirm the robustness of our approach against occlusion and illumination changes.

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.

This manuscript introduces a new technique for autonomous segmentation of color images in Red-Green-Blue (RGB) space that makes use of lattice auto-associative memories (LAAMs). LAAMs are artificial neural networks able to store a finite set X of pattern vectors and retrieve them almost correctly when a noisy or incomplete input is presented. Two dual lattice auto-associative memories have been...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

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...