Study on the Prediction of Real estate Price Index based on HHGA-RBF Neural Network Algorithm

The traditional error of the back-propagation algorithm multilayer feed-forward network (BP neural network), there are the flaws of a slow convergence of forecast, getting local minimum solutions easily, and forecast accuracy rate is not high. This paper proposes a new approach which is the combination of hierarchical genetic algorithm and least squares method to optimize the RBF neural network such that we can predict the real estate price of the Real estate Price Index. And which overcomes the shortcomings of traditional Fourier analysis, has good localized characteristics in the time domain and frequency domain, and has important value. In signal processing, image processing, voice analysis and other fields. The hierarchical genetic algorithm is usually used to optimize the topology of the RBF neural network, the radial basis function center and width. Alternatively, the least squares method could play an important role in deciding the weights of the output layer. The experimental result shows that the feasibility of RBF neural network which could be optimized by the hybrid hierarchical genetic algorithm to predict real estate closing price, and the superiority of this approach in the aspect of prediction accuracy verified in comparing with the other two methods.