A Learning Algorithm Based on PSO and L-M for Parity Problem
Yang Guangyou, Daode Zhang · 2006
Despite of the many successful applications of backpropagation (BP), it has many drawbacks. For complex problems, it may require a long time to train the networks, and it may run into local minima, and it may not train at all. Particle swarm optimization (PSO) algorithm is a global and stochastic algorithm based on population evolution which mode is simple, it is effective method for optimization of complex modeling. The paper uses PSO algorithm as learning algorithm of neural network used to solve parity problem. The PSO combined with Levenberg-Marquardt algorithm (modified BP algorithm) improve its performance. The simulation results show that this method not only increases the convergence rate of learning but it increases the likelihood of escaping from the local minima.