Fuzzy neural network algorithm integrating classification and clustering into one body

A fuzzy neural network and clustering technology, which is applied in biological neural network models, neural learning methods, neural architectures, etc., can solve problems such as ambiguity, irrationality, and super-box overlap
CN107451655AInactive Publication Date: 2017-12-08SHANGHAI DIANJI UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI DIANJI UNIV
Publication Date
2017-12-08
Estimated Expiration
Not applicable ยท inactive patent

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Abstract

The invention provides a fuzzy neural network algorithm integrating classification and clustering into one body. Each step is redesigned based on the fuzzy minimum-maximum neural network. In each step, the possibility of simultaneous existence of unidentified and identified samples is fully considered to distinguish the difference between classification learning and clustering learning, so that two learning methods coexist in the same fuzzy neural network system. If an input sample set is composed of all unidentified samples, an unsupervised learning mode is used to cluster the sample set. If the input sample set is composed of all identified samples or some identified samples and some unidentified samples, a supervised learning mode is used to classify the sample set. The algorithm provided by the invention can be used for pure clustering and pure classification, and can be used for a hybrid learning mode of clustering and classification. In the process of hybrid sample learning, both identified and unidentified samples are fully utilized, which improves the classification accuracy.
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Description

technical field

[0001] The invention relates to the technical field of pattern classification, in particular to a fuzzy neural network algorithm integrating "classification and clustering". Background technique

[0002] In the field of pattern classification, supervised and unsupervised learning has been playing a very important role. The so-called supervised learning, that is, the input data used as training samples has category information, which is called labeled samples; the main task of pattern classification (Classification) is to explore the decision boundary between a certain class and class, so that the category The misclassification rate is minimized. The unsupervised learning method is just the opposite. The input data used as training samples has no category information, which is called unlabeled samples (unlabeled); the main task of pattern clustering (Clustering) is to divide the input pattern according to a certain similarity criterion. Into several groups (...

Claims

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