Pseudoinverse Network |
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:
dW_{ik}= n . [ X_{i}^{u} - Sum_{j}(W_{ij} X_{j}^{u}) ] . X_{k}^{u} |
where W_{ik} is the weight between neuron i and neuron k, n is the learning rate, X_{i}^{u }is the target pattern, Sum_{j}(W_{ij} X_{j}^{u}) is the output of a linear neuron and X_{k}^{u} 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 -- mlh1@cornell.edu).
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.