MATLAB人工神经网络.doc

Artificial Neural Networks Abstract: The Artificial Neural Network (ANN) is a functional imitation of simplified model of the biological neurons and their goal is to construct useful ‘computers’ for real-world problems and reproduce intelligent data evaluation techniques like pattern recognition, classification and generalization by using simple, distributed and robust processing units called artificial neurons. This paper will present a simple application of the artificial neural network: process, design and performance analysis. 1. Working process of Artificial Neural Networks An artificial neuron models the dendrites of a biological neuron by receiving one or more inputs, then applying appropriate weights (positive or negative multipliers) to the signals, and passes the weighted inputs to the soma. The soma or body of an artificial neuron, receives the weighted inputs and computes a single output signal (a discrete or continuous function of the sum of the weighted inputs) by applying a threshold or transfer function (Yoon, 1989). The last section of each artificial neuron is analogous to the axon of a biological neuron, and passes the output signal to one or more subsequent artificial neurons or to the outside world. Every non-trivial artificial neural network contains an input layer and an output layer. Most also contain one or more intermediate processing layers. Types of Neural Networks: Both feed-forward and recurrent networks are examples of supervised learning. In unsupervised learning, no external teacher is required. The system self-organizes the input data, discovering for itself the regularities and collective properties of the data. These feed-forward networks have the ability to learn. To do so, an artificial neural network must learn to produce a desired output by modifying the weights from its inputs. The process of how this is done is simple. 2. Problems A. 9 training samples, 361 testing samples. B. 9 training samples, 361 testing samples