Cluster-based self-organizing neuro-fuzzy system with hybrid learning approach for function approximation

A novel hybrid cluster-based self-organizing neuro-fuzzy system (HC-SONFS) is proposed for dynamic function approximation and prediction. With the mechanism of self-organization, fuzzy rules are generated in the form of clusters using the proposed self-organization method to achieve compact and sufficient system structure if the current structure of knowledge base is insufficient to satisfy the required performance. A hybrid learning algorithm combining the well-known random optimization (RO) and the least square estimation (LSE) is use for fast learning. An example of chaos time series for system identification and prediction is illustrated. Compared to other approaches, excellent performance of the proposed HC-SONFS is observed.