An Efficient Deep Belief Network with Fuzzy Learning for Nonlinear System Modeling

A deep belief network (DBN) is one of the most effective ways to realize a deep learning technique, and has been attracting more and more attentions in nonlinear system modeling. However, it can not provide satisfactory results in learning speed and modeling accuracy, which is mainly caused by gradient diffusion. To address these problems and promote its development in cross-models, we propose an efficient DBN with a fuzzy neural network (DBFNN) for nonlinear system modeling. In this novel framework, DBN is considered as a pre-training technique to realize fast weight-initialization and to obtain a feature-representation vector. An FNN-based learning framework is developed for supervised modeling so as to eliminate the gradient diffusion issue, where its input happens to be the feature-representation vector. As a novel cross-model, DBFNN combines the advantages of both pre-training technique of DBN and an FNN model to improve nonlinear system modeling capability. A classical benchmark problem is used to demonstrate its superiority over existing single-models in learning speed and modeling accuracy.