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Architecture and Weight Optimization of ANN Using Sensitive Analysis and Adaptive Particle Swarm Optimization


Faisal Muhammad Shah, Khairul Hasan, Mohammad Moinul Hoque, Suman Ahmmed


Vol. 10  No. 8  pp. 103-111


This paper presents an optimized architecture and weights of three layered ANN designing method using sensitivity analysis and adaptive particle swarm optimization (SA?APSO). The optimized ANN architecture determination means to look for near minimal number of neurons in the ANN and finding the efficient connecting weights of it in such a way so that the ANN can achieve better performance for solving different problems. The proposed algorithm designs the ANN into two phases. In the first phase it tries to prune the neurons from ANN using sensitivity analysis to achieve the near minimal ANN structure and therefore it tries to optimize the weight matrices for further performance enhancement by adaptive particle swarm optimization. In the SA phase the authors use impact factor and correlation coefficients for pruning lower salient neurons. Initially it tries to prune the neurons having less impacts in the performance of ANN based on their impact factor values. Therefore it tries to lessen more neurons through merging the similar neurons in the ANN using correlation coefficient among the neuron pairs. In the optimization part it applied adaptive particle swarm optimization to optimize the connecting weight matrices to attain better performance. In the optimization by APSO, a special type of PSO, the authors’ use training and validation fitness functions to emphasis on avoiding overfitting and more adapted with ANN, and to achieve effective weight matrices of ANN. To evaluate SA?APSO, it is applied on the dataset of Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA) to do short term load forecasting (STLF). Results show that the proposed SA-APSO is able to design smaller architecture and attain excellent accuracy.


Artificial neural networks, overfitting, correlation coefficients, particle swarm optimization