By Topic

A probabilistic rule-based system in artificial neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kozato, F. ; Imperial Coll., London, UK ; De Wilde, P.

The authors propose a hybrid system in which a probabilistic rule-based system is implemented as a forward chaining inference machine. Based upon this design, the rule-based system could be equipped with a generalization function, automatic rule learning functions and a damage tolerant feature. The system contains three networks, Hopfield binary neural network, a single-layered feedforward neural network and a multi-layered feedforward neural network, and operates in two distinctive phases for the network training and inference operations. In the training phase, a set of common knowledge pieces as information units and the inference rules used to derive new information units are both implemented in the system in order to assign a certain problem domain to the system. In the inference phase, the system accepts information units from the user and infers new information units, according to the common knowledge and the rules

Published in:

Artificial Neural Networks, 1991., Second International Conference on

Date of Conference:

18-20 Nov 1991