Abstract
In this paper a method to optimize the structure of neural network named as Adaptive Particle Swarm Optimization (PSO) has been proposed. In this method nested PSO has been used. Each particle in outer PSO is used for different network construction. The particles update themselves in each iteration by following the global best and personal best performances. The inner PSO isused for training the networks and evaluate the performance of the networks. The effectiveness of this method is tested on many benchmark datasets to find out their optimum structure and the results are compared with other population based methods and finally the optimum structure is implemented using Modified Teaching Learning Based Optimization(MTLBO)for classification using neural network in data mining.
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