Fundamentals of Artificial Neural Networks Presentation-1 By Dr. Pankaj Agarwal IMS Engineering College
DEFINITION OF NEURAL NETWORKS According to the DARPA Neural Network Study (1988, AFCEA International Press, p. 60): • ... a neural network is a system composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes. According to Haykin (1994), p. 2: A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: • Knowledge is acquired by the network through a learning process. • Interneuron connection strengths known as synaptic weights are used to store the knowledge.
❑ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. ❑The amazing thing about a neural network is that you don't have to program it to learn explicitly: it learns all by itself, just like a brain! ❑An artificial neural network (ANN), usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks.
WHY ANN? • Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. • A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.