Associative memory for intelligent control

In many industrial applications of softcomputing, intelligent controls are important to accomplish high level tasks. Intelligent controls, however, need specific knowledge for each task. Therefore developing good memory is crucial to store the required knowledge efficiently and robustly. Neural network associative memories are the most suitable for the role because of their flexibility and content addressability. In this paper, first, we describe the basic concept of the neural network associative memories and the conventional learning algorithms. After pointing out some problems of the associative memories, we explain a novel learning algorithm, which is superior to the conventional ones. Finally, we introduce an associative memory suited for the intelligent controls and show the effectiveness by a number of computer simulations.