  Neural Networks have been applied for time series prediction with good
experimental results that indicate the high capacity to approximate functions
with good precision. Most neural models used in these applications use
activation functions with fixed parameters. However, it is known that the
choice of activation function strongly influences the complexity and
performance of the neural network and that a limited number of activation
functions have been used. In this work, we propose the use of a family of free
parameter asymmetric activation functions for neural networks and show that
this family of defined activation functions satisfies the requirements of the
universal approximation theorem. A methodology for the global optimization of
this family of activation functions with free parameter and the weights of the
connections between the processing units of the neural network is used. The
central idea of the proposed methodology is to simultaneously optimize the
weights 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 improving
performance in the adjustment and forecasting of time series. We chose two
learning algorithms: backpropagation with the term momentum (BPM) and
LevenbergMarquardt (LM).
