Embedding Neural Network in Knowledge Acquisition System
Main Article Content
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.
Â
Â
Key Words: neural network, knowledge acquisition, knowledge extraction, rules, back propagation algorithm.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.