Successive learning in hetero-associative memories using chaotic neural networks

In this paper, the authors propose a successive learning method in hetero-associative memories such as bidirectional associative memories and multidirectional associative memories using chaotic neural networks. It can distinguish unknown data from the stored known data and can learn the unknown data successively. The proposed model makes use of the difference in the response to the input data in order to distinguish unknown data from the stored known data. When input data is regarded as unknown data, the data is memorized. Furthermore, the proposed model can estimate and learn correct data from noisy unknown data or incomplete unknown data by considering the temporal summation of the continuous data input. In addition, similarity to the physiological facts in the olfactory bulb of a rabbit, as found by Freeman (1991), is observed in the behavior of the proposed model. A series of computer simulations shows the effectiveness of the proposed model.