Providing a method to model and estimate equations using artificial neural network

The purpose of this article is Providing a method to model and estimate equations using artificial neural network. Estimating equations have been successfully used to study complex systems when it is difficult to define a likelihood function. The basis of the method is to have, or to find, a set of simultaneous equations involving both the sample data and the unknown model parameters which are to be solved in order to define the estimates of the parameters. Various components of the equations are defined in terms of the set of observed data on which the estimates are to be based. Neural networks are a class of machine learning algorithms inspired by the structure and functioning of the human brain. The results of the simulation show that the neural network hybrid computational intelligence model based on the genetic algorithm is superior to other methods in terms of all performance evaluation criteria. Based on the obtained results, the model of combined computational intelligence has less error and thus more efficiency in estimating equations.