A classifier based on mixed radial basis function network and combinatorial optimization model for medical diseases diagnosis

Abstract

The Mixed Radial Basis Function Neural Network (MRBFNN) is an artificial neural network that employs Radial Basis Functions (RBFs) as activation functions in its hidden layer. The number of neurons in the hidden layer and the choice of RBF functions used in these neurons significantly affect the convergence of MRBFNN learning algorithms and impact the overall performance of neural networks. This article presents a nonlinear optimization model and an algorithm to select an appropriate architecture and learning strategy for MRBFNN. To approximate the solution of our model, we utilized an algorithm based on Particle Swarm Optimization (PSO) techniques. We will apply our approach in Medical Diseases Diagnosis (MDD). The numerical results obtained demonstrate the effectiveness of the proposed theoretical approach and underscore the advantages of the new modeling methodology.