TAF PUBLISHING

The purpose of this chapter is to introduce a powerful class of mathematical models: the artificial neural networks. This is a very general term that includes many different systems and various types of approaches, both from statistics and computer science. The analogy is not very detailed, but it serves to introduce the concept of parallel and distributed computing. Neural networks explain and give the proper demonstration for the applications of neural networks and wireless network. The artificial network and wireless network have strong connection and can be investigated and explained properly. There are also some models which are called mathematical models which can explain and demonstrate the artificial and wireless networks in detail. There are many organizations which are using artificial neural networks by using sensor technology for wide range of purpose. Then we analyze in detail a widely applied type of artificial neural network: the feed-forward network with error back-propagation algorithm. We illustrate the architecture of the models, the main learning methods and data representation, showing how to build a typical artificial neural network. Our aim is not to examine them all (it would be a very long discussion), but to understand the basic functionality and the possible implementations of this powerful tool. We initially introduce neural networks, by analogy with the human brain.