A search engine based on neural correlation matrix memories
This paper describes a novel search and pattern matching technology, AURA, based on neural networks. The technology exploits the simple and fast training of correlation matrix memories and their ability to match noisy and incomplete data. Unlike other neural network approaches, the method scales well to accommodate very large datasets, and has a simple and effective hardware implementation. It achieves this by storing basic feature data and relying on the fast matching ability of correlation matrix memory methods, as well as a two-stage processing approach. The paper demonstrates that the technology is applicable to a wide range of problems and shows how it has been implemented in dedicated hardware for high-performance applications.
search engines correlation matrix memory aura neural networks pattern recognition