Evaluating the number of hidden neurons necessary for solving of pattern recognition and classification tasks is one of the key problems in artificial neural networks. Multilayer perceptron is the most useful artificial neural network to estimate the functional structure in classification. In this paper, we show that artificial neural network with a two hidden layer feed forward neural network with d inputs, d neurons in the first hidden layer, 2d+2 neurons in the second hidden layer, k outputs and with a sigmoidal infinitely differentiable function can solve classification and pattern problems with arbitrary accuracy. This result can be applied to design pattern recognition and classification models with optimal structure in the number of hidden neurons and hidden layers. The experimental results over well-known benchmark datasets show that the convergence and the accuracy of the proposed model of artificial neural network are acceptable. Findings in this paper are experimentally analyzed on four different datasets from machine learning repository.

In case of decision making problems, identification of non-linear systems is an important issue. Identification of non-linear systems using a multilayer perceptron (MLP) trained with back propagation becomes much complex with an increase in number of input data, number of layers, number of nodes, and number of iterations in computation. In this paper, an attempt has been made to use fuzzy MLP and its learning algorithm for identification of non-linear system. The fuzzy MLP and its training algorithm which allows to accelerate a process of training, which exceeds in comparing with classical MLP is proposed. Results show a sharp reduction in search for optimal parameters of a neuro fuzzy model as compared to the classical MLP. A training performance comparison has been carried out between MLP and the proposed fuzzy-MLP model. The time and space complexities of the algorithms have been analyzed. It is observed, that number of epochs has sharply reduced and performance increased compared with classical MLP.