Evaluating the investment risk of electrical project based on particle swarm optimization with support vector machine optimized

In this paper, we use particle swarm optimization with support vector machine optimized to evaluate the investment risk of electrical project. A hybrid intelligent system is applied to evaluation of electrical equipment, combining particle swarm optimize algorithm (PSO) and support vector machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general recognizing ability of SVM. And then, these parameters are used to develop classification rules and train SVM. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.