This paper presents a new sliding mode control algorithm that is able to guide the trajectory of a multi-layer perceptron within the plane formed by the two objective functions: training set error and norm of the weight vectors. The results show that the neural networks obtained are able to generate an approximation to the Pareto set, from which an improved generalization performance model is selected. c � 2002 Elsevier Science B.V. All rights reserved.
['Marcelo Azevedo Costa', 'Antônio de Pádua Braga', 'Benjamin Rodrigues de Menezes', 'Roselito de Albuquerque Teixeira', 'G.G. Parma']
Training neural networks with a multi-objective sliding mode control algorithm