Applications of information storage matrix neural networks

Abstract

Information storage matrices (ISM) have recently been introduced as artificial neural networks. They define a new parallel processing architecture in which the neural connection weights are efficiently trained through a global learning strategy. This eliminates a need for slowly converging iterative learning schemes found in most present day neural network paradigms. Consequently, the ISM neural networks are attractive for real-time and online applications. Several such opportunities are discussed in this presentation. First it is demonstrated how the Boolean logic is implemented, and how the basic digital computer operations are realized through an inherently analog ISM organization. The ISM neural network is then trained to perform a pattern recognition task. The specified feature is extracted from a binary input data string. In addition, the ISM networks are used in the data processing and process control applications. In the first instance, spectral components of the covariance matrix are obtained without having to solve the characteristic equation. In the second case, process performance is stabilized on-line with respect to the initial state selected. As a result of such diverse application potentials, the ISM neural network is emerging as a useful parallel computing processor.