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Incomplete data fuzzy clustering method for information feedback RBF network estimations

A fuzzy clustering method and RBF network technology, applied in biological neural network models, systems based on fuzzy logic, character and pattern recognition, etc., can solve problems such as fuzzy clustering of incomplete data sets that cannot be directly applied

Inactive Publication Date: 2018-12-18
LIAONING UNIVERSITY
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Problems solved by technology

[0004] Aiming at the problem that the basic FCM algorithm cannot be directly applied to the fuzzy clustering of incomplete data sets, the present invention proposes an incomplete data fuzzy clustering method for information feedback RBF network estimation

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  • Incomplete data fuzzy clustering method for information feedback RBF network estimations
  • Incomplete data fuzzy clustering method for information feedback RBF network estimations
  • Incomplete data fuzzy clustering method for information feedback RBF network estimations

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Embodiment Construction

[0087] 1) Propose the information feedback RBF network model, (abbreviated as IFRBF network): combined with the Kalman filter method, the input parameters are X=(x 1 ,x 2 ,...,x n+m ), the output parameter is Y=(y 1 ,y 2 ,...,y m ), calculate the error e between the theoretical expected output value of the incomplete data and the actual output value of the network, and thus feed back the difference between the predicted value of the RBF neural network and the theoretical expected value of the data to the input layer, thereby obtaining the information feedback RBF The network is the IFRBF model.

[0088] The specific method is:

[0089] 1.1) Normalization of the input data set: convert all the data into numbers between the interval [0,1], thereby eliminating the difference in magnitude between the dimensions;

[0090] 1.2) Initialize the IFRBF network: set the corresponding number n+m, l, m for the nodes of each layer of the network, initialize the weight w, the center ve...

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Abstract

The invention relates to an incomplete data fuzzy clustering method for information feedback RBF network estimations, which comprises the following steps: 1) presenting an information feedback RBF network model; 2) presenting an incomplete data fuzzy clustering method (IFRBF-FCM) of information feedback RBF value estimations; 3) selecting the corresponding training sample set for the incomplete data sample by using the nearest neighbor rule, and training the IFRBF network for each missing attribute by using the nearest neighbor training sample set, thereby realizing the estimation prediction of the missing attribute in the incomplete data sample and obtaining the complete data set after the estimation recovery of the IFRBF network; 4) determining the estimation interval of the attribute ofthe incomplete data to propose an incomplete data fuzzy clustering method (IFRBF-IFCM) of IFRBF interval estimations to obtain fuzzy clustering results. The invention adopts the IFRBF network to estimate the incomplete data set and recovers the intact data set. Compared with the comparison method, the clustering result of the intact data set is more accurate than that of numerical type estimations, and the robustness is better.

Description

technical field [0001] The invention relates to a method for fuzzy clustering of incomplete data, in particular to an incomplete data fuzzy clustering method for information feedback RBF network interval estimation. Background technique [0002] With the rapid development of information technology, there is a large amount of data in various fields. And the processing of these data is beyond the human capabilities. Therefore, computers are needed to process these data. Cluster analysis has had a great impact in many fields. The traditional clustering analysis algorithm belongs to hard division, and each data sample can only belong to or not belong to a certain type of cluster. In other words, the membership degree of each type of cluster is either 0 or 1. However, most of the data in reality will have a certain degree of ambiguity, and do not strictly and clearly belong to a certain cluster, but belong to multiple clusters to varying degrees. [0003] Therefore, as an uns...

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Application Information

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IPC IPC(8): G06K9/62G06N7/02G06N3/04
CPCG06N7/02G06N3/044G06F18/23213G06F18/214
Inventor 张利石振桔张皓博刘洋王彦杰肖雪冬王军
Owner LIAONING UNIVERSITY
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