Further improvements on extreme learning machine for interval neural network

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 t...

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Bibliographic Details
Published in:Neural computing & applications 2018-04, Vol.29 (8), p.311-318
Main Authors: Yang, Li-fen, Liu, Chong, Long, Hao, Ashfaq, Rana Aamir Raza, He, Yu-lin
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Fuzzy logic
Fuzzy sets
Image Processing and Computer Vision
Iterative methods
Learning
Neural networks
Original Article
Performance prediction
Probability and Statistics in Computer Science
Regression analysis
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2727-4