Embedding Neural Network in Knowledge Acquisition System

Abstract

In artificial neural networks, the knowledge stored as the strength of the interconnection weights is modified through a process
called learning, using a learning algorithm. This algorithmic function, in conjunction with a learning rule, (i.e., back-propagation) is used to
modify the weights in the network in an orderly fashion. In this proposed system a technique is used for extracting business knowledge from
trained ANNs. It is organized into four sections that include acquisition of business data, knowledge extraction, representation by rules, and
Controller for maintain the consistency of knowledge. The technique will use Back-propagation NN to predict stock prices and stock
performance based on input of external variants such as government policies, quarterly export volumes etc. The application will also provide
recommendations (or decisions) based on expected outcome, overall customer portfolio, and current market situation.

Key Words: neural network, knowledge acquisition, knowledge extraction, rules, back propagation algorithm.