Quantum Particle Swarm Optimization Tuned Artificial Neural Network Equalizer

Abstract

This article uses Artificial Neural Network (ANN) trained with Quantum behaved Particle Swarm Optimization (QPSO) for the problem of equalization. Though the use of PSO in training of ANN finds optimal weights of the network it fails in the design of appropriate topology. But, QPSO is capable of optimizing the network topology. Here, parameters like neurons in each layer, the number of layers etc. are optimized using QPSO. Then, this QPSO tuned ANN then applied to the problem of channel equalization. The superior performance of proposed equalizer is proved through simulations.