The pseudoinverse rule is a simple supervised learning algorithm in a Hopfield-like neural network. This associative memory system is based on learning rule inspired from Adaline:
|dWik= n . [ Xiu - Sumj(Wij Xju) ] . Xku|
where Wik is the weight between neuron i and neuron k, n is the learning rate, Xiu is the target pattern, Sumj(Wij Xju) is the output of a linear neuron and Xku is the actual output. The iterative learning rule converges to a connectivity matrix which is known as the pseudoinverse.
The applet was written by Olivier Michel (adapted from Matt Hill -- email@example.com).
Use the mouse to enter a pattern by clicking squares inside the rectangle "on" or "off". Then, have the network store your pattern by pressing "Memorize". After storing some patterns (typically two), try entering a new pattern which you will use as a test pattern. Do not impose this new pattern, but use it as an initial state of the network. Press "Test" repeatedly to watch the network settle into a previously imposed state.