VC Dimension Based Fuzzy Sigmoid Neural Network (VC-FSNN)

In a supervised learning system the combination of fuzzy and neural network (NN) balancing the complexity of the network by the modification of membership functions and the hidden nodes. This paper presents a neural network for classification based on Vapnik Chervonenkis Dimensions (VC) for the structuring of hidden layers. A strong mathematical structure with a quartic equation is developed in this work to optimize the lower bound for the hidden nodes of the network. Experimentally it is analyzed with Thyroid dataset from UCI Machine Learning Repository using MATLAB R2018 (a) software.