Using the cascade-correlation algorithm to evaluate investment projects

This paper considers some aspects of using a cascade-correlation network in the investment task in which it is required to determine the most suitable project to invest money. This task is one of the most often met economical tasks. In various bibliographical sources on economics there are described different methods of choosing investment projects. However, they all use either one or a few criteria, i.e., out of the set of criteria there are chosen most valuable ones. With this a lot of information contained in other choice criteria is omitted. A neural network enables one to avoid information losses. It accumulates information and helps to gain better results when choosing an investment project in comparison with classical methods. The cascade-correlation network architecture that is used in this paper has been developed by Scott E. Fahlman and Cristian Lebiere at Carnegie Mellon University.