An adaptive least squares algorithm for the efficient training of artificial neural networks

A novel learning algorithm is developed for the training of multilayer feedforward neural networks, based on a modification of the Marquardt-Levenberg least-squares optimization method. The algorithm updates the input weights of each neuron in the network in an effective parallel way. An adaptive distributed selection of the convergence rate parameter is presented, using suitable optimization strategies. The algorithm has better convergence properties than the conventional backpropagation learning technique. Its performance is illustrated, using examples from digital image halftoning and logical operations such as the XOR function. >


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