A fast recursive neuronal learning algorithm applied to intelligent control

This paper presents as a contribution the formulation of the recursive least squares neuronal (RLSN) learning algorithm for teaching a neural network of the multilayer perceptron type. With this algorithm, the time of convergence achieved in the neural network is less than the time of convergence achieved when teaching the network with gradient methods. The procedure allows the application of neural network techniques to nonlinear and time dependant systems which are difficult to model and control