A generalized feedforward neural network architecture and its training using two stochastic search methods
Faculty of Computing, Health and Science
School of Engineering and Mathematics
Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks in which the synaptic interactions are mediated via a nonlinear mechanism called shunting inhibition, which allows neurons to operate as adaptive nonlinear filters. In this article, The architecture of SIANNs is extended to form a generalized feedforward neural network (GFNN) classifier. Two training algorithms are developed based on stochastic search methods, namely genetic algorithms (GAs) and a randomized search method. The combination of stochastic training with the GFNN is applied to four benchmark classification problems: the XOR problem, the 3-bit even parity problem, a diabetes dataset and a heart disease dataset. Experimental results prove the potential of the proposed combination of GFNN and stochastic search training methods. The GFNN can learn difficult classification tasks with few hidden neurons; it solves perfectly the 3-bit parity problem using only one neuron.