Particle Swarm Optimization Based Higher Order Neural Network for Classification

The maturity in the use of both the feed forward neural network and Multilayer perception brought the limitations of neural network like linear threshold unit and multi-layering in various applications. Hence, a higher order network can be useful to perform nonlinear mapping using the single layer of input units for overcoming the drawbacks of the above-mentioned neural networks. In this paper, a higher order neural network called Pi-Sigma neural network with standard back propagation Gradient descent learning and Particle Swarm Optimization algorithms has been coupled to develop an efficient robust hybrid training algorithm with the local and global searching capabilities for classification task. To demonstrate the capacity of the proposed PSO-PSNN model, the performance has been tested with various benchmark datasets from UCI machine learning repository and compared with the resulting performance of PSNN, GA-PSNN. Comparison result shows that the proposed model obtains a promising performance for classification problems.