Numerical solution of wavelet neural network learning weights using accelerated particle swarm optimization algorithm

Artificial Neural Network (ANN) is a supervised learning nonlinear complex model. This characteristic enables ANN to be used in nonlinear system modeling and classification applications. This research work proposed a technique called Wavelet Neural Network (WNN), and in the hidden layers of WNN, Morlet and Mexican are used as an activation function. During the processing, the WNN gets stuck in the local minimum causing slow convergence. For evaluating such kinds of problems, numerous algorithms have been tried and used. Consequently, this proposed research work used a novel meta-heuristic search technique called Accelerated Particle Swarm Optimization (APSO) algorithm combined with the WNN. Due to the effective convergence and fast searching toward an optimal solution, the APSO algorithm is used. In the proposed APSOWNN algorithm, APSO searches for the best sub-search solution. In conclusion, this model is assessed on the basis of total of three different datasets like the 4-bit OR, 7-bit Parity and IRIS benchmark classification problems, and its efficiency is equated with criterion methods such like Wavelet Back Propagation Neural Network (WBPNN), Artificial Bee Colony Wavelet Neural Network (ABCWNN) and WNN. Finally, from the results of simulation, it has been concluded that the proposed algorithm's performance is much better, as compared to the state-of-the-art algorithms in terms of mean square error.(MSE) and accuracy.