A hybrid PSO-GA based Pi sigma neural network (PSNN) with standard back propagation gradient descent learning for classification
Janmenjoy Nayak, Bighnaraj Naik, Himansu Sekhar Behera · 2014
Due to the strong global optimization capability and fast convergence, PSO has shown its efficiency in solving various real world benchmark applications. But premature convergence is one of the major drawback of PSO. In this paper to address this issue, a hybrid PSO-GA based Pi-sigma neural network with standard back propagation gradient descent learning (PSO-GA-PSNN) has been proposed for classification problems. The adjustment of algorithmic parameters is iteratively used until the error is less than the desired output. The proposed PSO-GA-PSNN has been tested with various benchmark datasets taken from UCI machine learning repository and the simulated results are being tested with the statistical tool ANOVA to show the obtained results are statistically steady and valid.