On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation

• Published in: IEEE transaction on neural networks and learning systems Vol. 26; no. 7; pp. 1417 - 1430
• Main Authors: Wong, Shen Yuong, Yap, Keem Siah, Yap, Hwa Jen, Tan, Shing Chiang, Chang, Siow Wee
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Benchmarking; Classification; Computational modeling; Databases, Factual; Extreme learning machine (ELM); Feedback; fuzzy inference system (FIS); Fuzzy Logic; Machine Learning; Models, Statistical; Neural Networks, Computer; Neurons; pattern classification; Power Plants; Pragmatics; Reproducibility of Results; rule based; Training; Extreme learning machine (ELM); pattern classification; rule based; fuzzy inference system (FIS)

This paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, three parameters of F-ELM are randomly assigned. The...