Comparison of Different Learning Algorithms for Pattern Recognition with Hopfield's Neural Network

Hopfield neural networks can be used for compression, approximation, steering. But they are most commonly used for pattern recognition thanks to their associative memory trait. In order to fulfill this task, the network has to be trained with one of algorithms. In this paper I will try to implement three of the most popular ones and compare their effectiveness by trying to recognize various patterns consisting of binary input arrays. The tests will use Hebbian learning, Oja's Hebbian modification and pseudo-inverse, which proves to be most promising training algorithm.