TNFIS: Tree-based neural fuzzy inference system

The restricted structure of fuzzy grid type based partitioning commonly employed in fuzzy model is limiting the fuzzy model on the whole to accurately describe the underlying distribution of data points in feature space. Common solution via the use of more linguistic terms to finely describe the feature space would confute the whole idea of introducing approximate reasoning. This paper proposes the TNFIS (tree-based neural fuzzy inference system) that integrates a decision tree based classification algorithm for identification of weighted rule base. The learning algorithm is fast and highly intuitive. Simulation result of a nonlinear process modelling shows that TNFIS is able to set up reasonable membership functions and generate concise rule base to approximate a desired data set. Comparison with earlier works shows that our model performs better or comparable to other models.