University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Knowledge incorporation into neural networks from fuzzy rules

Jin, Y and Sendhoff, B (1999) Knowledge incorporation into neural networks from fuzzy rules Neural Processing Letters, 10 (3). 231 - 242. ISSN 1370-4621

NPL10-3.pdf - Accepted Version
Available under License : See the attached licence file.

Download (569kB)
PDF (licence)

Download (33kB)


The incorporation of prior knowledge into neural networks can improve neural network learning in several respects, for example, a faster learning speed and better generalization ability. However, neural network learning is data driven and there is no general way to exploit knowledge which is not in the form of data input-output pairs. In this paper, we propose two approaches for incorporating knowledge into neural networks from fuzzy rules. These fuzzy rules are generated based on expert knowledge or intuition. In the first approach, information from the derivative of the fuzzy system is used to regularize the neural network learning, whereas in the second approach the fuzzy rules are used as a catalyst. Simulation studies show that both approaches increase the learning speed significantly.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Date : 1999
Identification Number : 10.1023/A:1018784510310
Additional Information : The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 03 May 2012 08:25
Last Modified : 23 Sep 2013 19:25

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800