Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78253
Title: Neural logic network learning using genetic programming
Authors: Tan, C.L. 
Chia, H.W.K. 
Issue Date: 2001
Citation: Tan, C.L.,Chia, H.W.K. (2001). Neural logic network learning using genetic programming. IJCAI International Joint Conference on Artificial Intelligence : 803-808. ScholarBank@NUS Repository.
Abstract: Neural Logic Network or Neulonet is a hybrid of neural network expert systems. Its strength lies in its ability to learn and to represent human logic in decision making using component net rules. The technique originally employed in neulonet learning is backpropagation. However, the resulting weight adjustments will lead to a loss in the logic of the net rules. A new technique is now developed that allows the neulonet to learn by composing net rules using genetic programming. This paper presents experimental results to demonstrate this new and exciting capability in capturing human decision logic from examples. Comparisons will also be made between the use of net rules, and the use of standard boolean logic of negation, disjunction and conjunction in evolutionary computation.
Source Title: IJCAI International Joint Conference on Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/78253
ISSN: 10450823
Appears in Collections:Staff Publications

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