Block-recursive least squares technique for training multilayer perceptrons

The multilayer perceptron is one of the most commonly used types of feedforward neural networks and it is used in a large number of applications. Its stength resides in its capacity of mapping arbitrarily complex nonlinear functions by a convenient number of layers of sigmoidal nonlinearities (Rumelhart et al., 1986). the back propagation algorithm is still the most used learning algorithms; it consists in the minimization of the Mean squared error (MSE) at the network output performed by means of a gradient descent on the error surface in the space of weights. the backpropagation algorithm suffers from a number of shortcomings; above all the relatively slow rate of convergence and the final misadjustment that cannot guarantee the success of the training procedure in real applications. the choice of a different learning algorithm, based on a different minimization criterion, can help to overcome these drawbacks (see Azimi-Sajadi et al., 1992) and (scalero et al., 1992) for some examples of LS-based fast learning algorithms). The BRLS training algorithm next described allows to obtain considerable improvements from the point of view of both the numerical accuracy and the speed of convergence