Higher order differential correlation associative memory of sequential patterns

This paper describes some properties of storage capacity and robustness of differential correlation associative memory of sequential patterns using higher order neural networks. First, it is shown that storage capacities for k=1, 2 and 3 dimensional cases are 0.059N, 0.023(₂^N) and 0.014(₃^N) from the prediction using the transition properties, respectively, where N is the number of neurons and (_K^N) means the combination of k from N. And it is shown that higher order models are superior in the pattern selection ability to the conventional one. Further, it is shown that higher order differential correlation models have high robustness compared to the conventional correlation models.