Genetic Neural Network Prediction of Car Ownership Based on Principal Component Analysis

摘要

The prediction of the car ownership is the basic work for city traffic sustainable development. The paper carry on principal component analysis to influencing factors in the process of prediction of Wuhan City car ownership, determine the main components. Combining genetic algorithm with neural network, using genetic algorithm to optimize the weights of neural network, determine the initial weight values of neural network. Not only to improve the neural network training speed and generalization ability, but also overcome that the network is easy to fall into local minimum to a certain extent, then train the neural network, and carry on the prediction of car ownership. At last use a specific example to verify the prediction effect.