One of possible solutions in creating an automatic system of face recognition is application of auto-associative neural networks for remembering and recognise two-dimensional face images. Experiments with applying the Hopfield network and twolayer perceptron confirmed the possibility of remembering and reproducing face images, even if partially covered or disturbed. Limited technical possibilit...

Cost estimation is a critical issue for software organizations. Good estimates can help us make more informed decisions (controlling and planning software risks), if they are reliable (correct) and valid (stable). In this study, we apply a variable reduction technique (based on auto-associative feed--forward neural networks – called Curvilinear component analysis) to log-linear regression funct...

The Little-Hopfield network is an auto-associative computational model of neural memory storage and retrieval. This model is known to robustly store collections of randomly generated binary patterns as stable-points of the network dynamics. However, the number of binary memories so storable scales linearly in the number of neurons, and it has been a longstanding open problem whether robust expo...

The Little-Hopfield network is an auto-associative computational model of neural memory storage and retrieval. This model is known to robustly store collections of randomly generated binary patterns as stable-states of the network dynamics. However, the number of binary memories so storable scales linearly in the number of neurons, and it has been a long-standing open problem whether robust exp...

A novel connectionist architecture that develops static representations of structured sequences is presented. The model is based on SRNs trained on an autoassociation task in a way that guarantees the development of unique static representations. The model can be applied in modeling Natural Language, cognition, etc.

The Willshaw model is asymptotically the most efficient neural associative memory (NAM), but its finite version is hampered by high retrieval errors. Iterative retrieval has been proposed in a large number of different models to improve performance in auto-association tasks. In this paper, bidirectional retrieval for the hetero-associative memory task is considered: we define information effici...

In general, neural networks are regarded as models for massively parallel computation. But very often, this parallelism is rather limited, especially when considering symmetric networks. For instance, Hoppeld networks do not really compute in parallel as their updating algorithm always requires sequential execution. Nevertheless, Hoppeld networks can be used as auto-associative memories, were s...

We consider application of neural associative memories to chemical image recognition. Chemical image recognition is identification of substance using chemical sensors' data. The primary advantage of associative memories as compared with feed-forward neural networks is highspeed learning. We have made experiments on odour recognition using hetero-associative and modular autoassociative memories....