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Neural Networks have been applied for time series prediction with goodexperimental results that indicate the high capacity to approximate functionswith good precision. Most neural models used in these applications useactivation functions with fixed parameters. However, it is known that thechoice of activation function strongly influences the complexity andperformance of the neural network and that a limited number of activationfunctions have been used. In this work, we propose the use of a family of freeparameter asymmetric activation functions for neural networks and show thatthis family of defined activation functions satisfies the requirements of theuniversal approximation theorem. A methodology for the global optimization ofthis family of activation functions with free parameter and the weights of theconnections between the processing units of the neural network is used. Thecentral idea of the proposed methodology is to simultaneously optimize theweights and the activation function used in a multilayer perceptron network(MLP), through an approach that combines the advantages of simulated annealing,tabu search and a local learning algorithm, with the purpose of improvingperformance in the adjustment and forecasting of time series. We chose twolearning algorithms: backpropagation with the term momentum (BPM) andLevenbergMarquardt (LM).

Otimizacao de pesos e funcoes de ativacao de redes neurais aplicadas na previsao de series temporais