A novel Chemical Reaction Optimization based Higher order Neural Network (CRO-HONN) for nonlinear classification

Abstract

In this paper, a Chemical Reaction Optimization (CRO) based higher order neural network with a single hidden layer called Pi–Sigma Neural Network (PSNN) has been proposed for data classification which maintains fast learning capability and avoids the exponential increase of number of weights and processing units. CRO is a recent metaheuristic optimization algorithm inspired by chemical reactions, free from intricate operator and parameter settings such as other algorithms and loosely couples chemical reactions with optimization. The performance of the proposed CRO-PSNN has been tested with various benchmark datasets from UCI machine learning repository and compared with the resulting performance of PSNN, GA-PSNN, PSO-PSNN. The methods have been implemented in MATLAB and the accuracy measures have been tested by using the ANOVA statistical tool. Experimental results show that the proposed method is fast, steady and reliable and provides better classification accuracy than others.

Keywords

• Higher Order Neural Networks (HONNs)
• Pi–Sigma neural networks
• Genetic Algorithm (GA)
• Particle Swarm Optimization (PSO)
• Chemical Reaction Optimization (CRO)