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Extreme TS fuzzy reasoning method and system based on extreme learning machine

An extreme learning machine and fuzzy technology, applied in the computer field, can solve problems such as long training time

Pending Publication Date: 2018-10-16
SHENZHEN UNIV
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Problems solved by technology

[0005] The main purpose of the present invention is to provide a construction method and system of an extreme TS fuzzy reasoning system, aiming to solve the technical problem of long training time in the TS fuzzy reasoning system in the prior art

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Embodiment Construction

[0022] In order to make the purpose, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described The embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present invention.

[0023] see figure 1 , is a schematic flow chart of an extreme TS fuzzy reasoning method based on an extreme learning machine in an embodiment of the present invention, and the method includes:

[0024] Step 101, using the K-means clustering algorithm to cluster the original conditional attribute value matrix corresponding to the training dat...

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Abstract

The invention discloses an extreme TS fuzzy reasoning method and system based on an extreme learning machine. The method includes the steps that initial-condition attribute value matrixes corresponding to training data sets are clustered with the K-means clustering algorithm, and expansion decision attribute value matrixes are established according to clustering results; a single extreme learningmachine is trained through the expansion decision attribute value matrixes, and the output layer weight and a trained extreme learning machine are obtained; new samples are input into the extreme learning machine, and the triggering strength of a fuzzy rule antecedent and the conclusion truth value of a fuzzy rule consequent are obtained; according to the triggering strength and the conclusion truth value, defuzzification is carried out, and forecasting output of the new samples is obtained. The single extreme learning machine is trained through the expansion decision attribute value matrixes,the training process can be rapidly completed through parameter optimization without iteration, training time is short, and through defuzzification operation based on the softmax function, normativeprocessing of the triggering strength can be effectively achieved, and outputting of forecasting output data is effectively achieved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to an extreme TS fuzzy reasoning method and system based on an extreme learning machine. Background technique [0002] The Takagi-Sugeno (TS) fuzzy reasoning system was first proposed by Japanese scholars T.Takagi and M.Sugeno, and was further improved by M.Sugeno and G.T.Kang. TS fuzzy reasoning is also called Sugeno fuzzy reasoning or Takagi-Sugeno-Kang (TSK ) fuzzy reasoning. Its core idea is "to use several simple linear systems to fit a complex nonlinear system", through the fuzzification of the input, the inference calculation based on fuzzy rules, and the defuzzification of the output to achieve "using a series of local linear model to approximate an overall nonlinear model". Theoretical research shows that TS fuzzy inference system can approximate any nonlinear model with arbitrary precision. [0003] The key point of building a TS fuzzy reasoning system is the determin...

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Application Information

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IPC IPC(8): G06N5/04G06K9/62
CPCG06N5/048G06F18/23213G06F18/214
Inventor 何玉林
Owner SHENZHEN UNIV
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