Combining Fuzzy Knowledge and Data for Neuro-Fuzzy Modeling

The aim of this paper is to contribute to a central issue in neural network that is of combining expert knowledge and observations (data) for learning. It is generally known that neural networks, as other adaptive models, have good learning and generalization capabilities because of their statistical consistency. However, such consistency is theoretically valid only for large size training sets. To enhance learning with small size sets, it is naturally desirable to incorporate additional knowledge (herein referred to as expert knowledge) in the architecture of the network to allow a task-oriented rather than just a generic problem-free architecture. In this paper we show how expert knowledge in terms of fuzzy rules can be incorporated into the architecture of a neural network. The parameters of the network are therefore adapted from the available data using a gradient descent learning algorithm which is an adaptation of backpropagation. The neuro-fuzzy system introduced is successfully applied to a decision problem in traac monitoring into an automated vehicles parking.