A new approach for classification has been presented in this paper. The proposed technique, Modified Radial Basis Functional Neural Network (MRBFNN) consists of assigning weights between the input layer and the hidden layer of Radial Basis functional Neural Network (RBFNN). The centers of MRBFNN are initialized using Particle swarm Optimization (PSO) and variance and centers are updated using back propagation and both the sets of weights are updated using Recursive Least Square (RLS). Our simulation result is carried out on Wisconsin Breast Cancer (WBC) data set. The results are compared with RBFNN, where the variance and centers are updated using back propagation and weights are updated using Recursive Least Square (RLS) and Kalman Filter. It is found the proposed method provides more accurate result and better classification.
Senapati, Manas Rajan; Routray, P. K.; and Dash, Pradipta Kishore
"Breast Cancer Detection using Recursive Least Square and Modified Radial Basis Functional Neural Network,"
International Journal of Computer and Communication Technology: Vol. 3:
1, Article 11.
Available at: https://www.interscience.in/ijcct/vol3/iss1/11