ARTIFICIAL NEURAL NETWORKS: REVIEW

Artificial neural networks (ANNs) are computer softwares that were developed by simulating the working mechanism of human brain to accomp lish the basic functions of the brain. ANNs have capability to learn, remember and then generalize the data to produce new i nformation, and to detect the relationships between variables. There are considerable relations between the statistical methods and the neural networks. In the present study, biological neural network and neurons of the human brain and the general structure of ANNs were intr oduced. Then ANNs’ relations with the statistical methods were inve stigated. ANNs’ advantages and disadvantages a s statistical methods were discussed. Many neural networks methods are considered gene ralizations of some of the classical statistical techniques. Generally, in statistics ANNs are used as flexible, nonlinear regression and classif ication models. Many neur al network architectures have close links with the nonparametric statistical methods. Results may be obtained by training the feed forward ANNs algorithms with the nonlinear models of many statistical techniques.