Optimization methodology applied to feed‐forward artificial neural network parameters

Abstract This article recommends a methodology for developing a neural network with great chances to be an optimal one. The method is based on trial and error in determining the internal parameters of the network considered as having a significant influence over its performance: the number of hidden layers, activation function, number of neurons in the hidden layers, training epochs, learning rate, and momentum term. This optimization methodology is presented in two separate sections: first of them contains a series of practical considerations recommended for neural network modeling, and the second is represented by the proposed optimization algorithm, formulated in six steps and based on the practical statements. Two case studies are chosen to exemplify the use of the algorithm for finding the near optimal neural network: the dependence of the reduced and intrinsic viscosities of the siloxane‐organic copolymers of the solution concentration, temperature, and copolymer type, differing by the siloxane sequence length. The two siloxane‐organic polyazomethines resulted by the reaction of a fully aromatic bisazomethine diol with α,ω‐bis(chloromethyl)oligodimethylsiloxanes. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2011