3-Layer Neural Network Classifier With the Heterogeneous Hidden Layers

Abstract

The ways to improve the performance of classifiers based on the bithreshold architecture are considered in the paper. The model of 3-layer neural network binary classifier is proposed whose first hidden layer consists of neural units of 3 kinds: bithreshold neurons, linear threshold units and winner-take-all neurons, and every neuron in the second layer has only 3 inputs with predefined weights. The synthesis algorithm for such networks is designed and estimations of its time complexity and the size of resulting network are presented. The simulation results demonstrate that the application of the new architecture in the classifier design significantly improves its generalization ability.