Risk Evaluation Of High-Technology Project Investment Based On AHP-ANN Model

There are many characteristics in high-technology project investment, such as needing a lot of capital, having so many unsure factors and so on. There are full of risks in the investment process. Some corporations were disastrous in investment because of ignoring the risk evaluation or using the inapposite evaluation methods. So it is necessary to use a scientific evaluation method in the investment of high-technology project.This paper attempts to structure a risk evaluation model of high-technology project investment based on the combination of AHP (Analytic Hierarchy Process) and ANN (Artificial Neural Network) in view of limitations of the existing evaluation methods and improving evaluation efficiency and validity. We first establish a comprehensive risk evaluation system with AHP and filtrate the evaluation indicators based on their importance. Then we make use of two types of neural networks that are BP(Back-propagation) and RBF(Radial Basis Function) to imitate evaluation process respectively by using MATLAB software. The results show that the AHP-ANN model based on combining AHP with RBF is better than AHP with BP both on efficiency and training speed.