Real Estate Investment Risk Assessment Based on GABP Algorithm of Neural Network

Zhenyu Zhao; Zhen Luo; Wei Zhang
February 2012
International Journal of Digital Content Technology & its Applic;Feb2012, Vol. 6 Issue 3, p122
Academic Journal
The back propagation neural network learning algorithm (Back Propagation, BP) is slow in training, easily falling into local minima and it's weak in global searching. The genetic algorithm(Genetic Algorithm, GA) belongs to a global search, so it's easy to find the optimal solution or sub-optimal solution. Using GA to optimize the initial weight and threshold of BP, then use BP to correct the weight and threshold of network to conduct a network training. In that way, BP network can be avoided falling into local minima and the network can also be optimized. This paper selects mass data of a number of estate development projects as training samples and test samples to build a real estate investment risk assessment model based on GABP and to conduct empirical analysis as well. This paper has important theoretical and practical significance in improving the ability to identify real estate investment risk and real estate risk management of the main involved.


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