Abstract
Feed-forward neural network models approximate nonlinear functions connecting inputs to outputs. The cascade correlation (CC) learning algorithm allows networks to grow dynamically starting from the simplest network topology to solve increasingly more difficult problems. It has been demonstrated that the CC network can solve a wide range of problems including those for which other kinds of networks (e.g., back-propagation networks) have been found to fail. In this paper we show the mechanism and characteristics of nonlinear function learning and representations in CC networks, their generalization capabilities, the effects of environmental bias, etc., using a variety of knowledge representation analysis tools.
