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Comparing Logic Programming in Radial Basis Function Neural Network (RBFNN) and Hopfield Neural Network


Affiliations
1 School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.
 

Neural network is a black box that clearly learns the internal relations of unknown systems. Neural-symbolic systems are based on both logic programming and artificial neural networks. Radial basis function neural network (RBFNN) and Hopfield neural network are the two well-known and commonly used types of feed forward and feedback networks. This study gives an overview of how logic programming is been carried out on both networks as well as the comparison of doing logic programming on both radial basis neural network and Hopfield neural network.
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  • Comparing Logic Programming in Radial Basis Function Neural Network (RBFNN) and Hopfield Neural Network

Abstract Views: 137  |  PDF Views: 91

Authors

Mamman Mamuda
School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.
Saratha Sathasivam
School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia.

Abstract


Neural network is a black box that clearly learns the internal relations of unknown systems. Neural-symbolic systems are based on both logic programming and artificial neural networks. Radial basis function neural network (RBFNN) and Hopfield neural network are the two well-known and commonly used types of feed forward and feedback networks. This study gives an overview of how logic programming is been carried out on both networks as well as the comparison of doing logic programming on both radial basis neural network and Hopfield neural network.

References