ABSTRACT
Artificial neural network (ANN) is one of the popular areas of artificial intelligence research and also an abstract computational model based on the organizational structure of the human brain. This chapter addresses basics of ANN architecture, paradigms of learning, activation functions, and leaning rules. The ability to learn and generalize from a set of training data is one of the most powerful features of ANN. The learning situations in neural networks may be classified into two types, namely, supervised and unsupervised. The chapter discusses the various types of learning rules for ANN: Error back-propagation learning algorithm or delta learning rule; Hebbian learning rule; Perceptron learning rule; Widrow–Hoff learning rule; Winner-Take-All learning rule; etc. The generalized delta learning rule propagates the error back by one layer, allowing the same process to be repeated for every layer.
