PSO Algorithm Combined with Neural Network Training Study

Neural network often is trained by multilayer feedforward neural network ago, but it may fall into local minimum point. In this article, swarm optimization particle is improved so that it can adapt to solve optimization problem of discrete variables. At the same time, introducing the crossover operation of genetic algorithm make it form hybrid particle swarm optimization. Then combining the method of neural network, weight training of neural network is transformed into function optimization. The error function is cited as the definition of particle fitness. Last, in the information filtering. The efficient is compared using the multilayer and particle swarm optimization.