Azimi-Sadjadi, Mahmood R., authorLiou, Ren-Jean, authorIEEE, publisher2007-01-032007-01-031992Azimi-Sadjadi, Mahmood R. and Ren-Jean Liou, Fast Learning Process of Multilayer Neural Networks Using Recursive Least Squares Method, IEEE Transactions on Signal Processing 40, no. 2 (February 1992): 446-450.http://hdl.handle.net/10217/934In this correspondence a new approach for the learning process of multilayer perceptron neural networks using the recursive least squares (RLS) type algorithm is proposed. This method minimizes the global sum of the squared errors between the actual and the desired output values iteratively. The weights in the network are updated upon the arrival-of a new training sample and by solving a system of normal equations recursively. To determine the desired target in the hidden layers an analog of backpropagation (BP) strategy used in the conventional learning algorithms is developed. This permits the application of the learning procedure to all the layers. Simulation results on the 4-b parity checker and multiplexer networks are obtained which indicate significant reduction in the total number of iterations when compared with those of the conventional and accelerated backpropagation (ABP) algorithms.born digitalarticleseng©1992 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.learning systemsleast squares approximationsneural netsText