Abstract
This paper introduced BP neural network and RBF network's basic theory, compared these two characteristics of the network structure, and applied to the resident consumer level forecasts. In RBF neural network forecasting, by changing the size of the distribution density of RBF, adjusted the forecast accuracy of the network. Compared the two neural network forecast results by MATLAB simulation. From the quantitative point proved that the RBF neural network is more efficient and accurate than BP neural network in forecasting the resident consumer level, and thus more suitable for practical application in guiding the design of neural network.
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