Title:
EXTREME LEARNING MACHINE-BASED EXTREME TS FUZZY INFERENCE METHOD AND SYSTEM
Document Type and Number:
WIPO Patent Application WO/2019/218263
Kind Code:
A1
Abstract:
An extreme learning machine-based extreme TS fuzzy inference method and system. The method comprises: clustering an original condition attribute value matrix corresponding to a training data set by means of a k-means clustering algorithm; constructing an expansion decision attribute value matrix according to the clustering result; training a single extreme learning machine by using the expansion decision attribute value matrix to obtain an output layer weight and the trained extreme learning machine; inputting a new sample into the trained extreme learning machine to obtain a premise firing strength and a qualified consequent of a fuzzy rule; and performing defuzzification according to the firing strength and the qualified consequent to obtain a predicted output of the new sample. A single extreme learning machine is trained by using an expansion decision attribute value matrix so that parameters which do not require iteration are optimized; the training process can be quickly completed; the training time is short; a softmax function-based defuzzification operation can efficiently standardize the firing strength; the predicted output data can be efficiently output.
Inventors:
HE YULIN (CN)
Application Number:
PCT/CN2018/087049
Publication Date:
November 21, 2019
Filing Date:
May 16, 2018
Export Citation:
Assignee:
UNIV SHENZHEN (CN)
International Classes:
G06K9/62; G06N5/04
Foreign References:
| CN107229973A | 2017-10-03 | |||
| CN107729943A | 2018-02-23 | |||
| CN1325088A | 2001-12-05 | |||
| CN107512267A | 2017-12-26 | |||
| JP2000339165A | 2000-12-08 | |||
| US20090005953A1 | 2009-01-01 |
Attorney, Agent or Firm:
HENSEN INTELLECTUAL PROPERTY FIRM (CN)
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