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). pp. 231-242.
NPL10-3.pdf - Accepted version Manuscript
Available under License : See the attached licence file.
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.
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Identification Number :||https://doi.org/10.1023/A:1018784510310|
|Additional Information :||The original publication is available at http://www.springerlink.com|
|Depositing User :||Symplectic Elements|
|Date Deposited :||03 May 2012 08:25|
|Last Modified :||23 Sep 2013 19:25|
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