Evolving fuzzy optimally pruned extreme learning machine for regression problems

This paper proposes an approach to the identification of evolving fuzzy Takagi–Sugeno systems based on the optimally pruned extreme learning machine (OP-ELM) methodology. First, we describe ELM, a simple yet accurate learning algorithm for training single-hidden layer feed-forward artificial neural networks with random hidden neurons. We then describe the OP-ELM methodology for building ELM models in a robust and simplified manner suitable for evolving approaches. Based on the previously proposed ELM method, and the OP-ELM methodology, we propose an identification method for selfdeveloping or evolving neuro-fuzzy systems applicable to regression problems. This method, evolving fuzzy optimally pruned extreme learning machine (eF-OP-ELM), follows a random projection based approach to extracting evolving fuzzy rulebases. In this approach systems are not only evolving but their structure is defined on the basis of randomly generated fuzzy basis functions. A comparative analysis of eF-OP-ELM is performed over a diverse collection of benchmark datasets against well known evolving neuro-fuzzy methods, namely eTS and DENFIS. Results show that the method proposed yields compact rulebases, is robust and competitive in terms of accuracy.