Further improvements on extreme learning machine for interval neural network

• DOI: 10.1007/s00521-016-2727-4
• Publication Date: 2016-12-07T23:52:55Z
• Authors: Li-fen Yang; Chong Liu; Hao Long; Rana Aamir Raza A...; Yu-lin He
• Keywords: Extreme Learning Machine

The interval extreme learning machine (IELM) (Yang et al. in Neural Comput Appl 27(1):3–8, 2016) is a newly proposed regression algorithm to deal with the data with interval-valued inputs and interval-valued output. In this paper, we firstly analyze the disadvantages of IELM and further point out that IELM is actually a slight variant of fuzzy regression analysis using neural networks (Ishibuchi and Tanaka in Fuzzy Sets Syst 50(3):257–265, 1992). Then, we propose a new interval-valued ELM (IVELM) model to handle the interval-valued data regression. IVELM does not require any iterative adjustment to network weights and thus has the extremely fast training speed. The experimental results on data sets used in (Yang et al. 2016) demonstrate the feasibility and effectiveness of IVELM which obtains the better predictive performance and faster learning speed than IELM.