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Pattern recognition method and device

A pattern recognition and fuzzy technology, applied in the field of pattern recognition, can solve the problems of poor convergence effect and low recognition accuracy, and achieve the effect of improving clustering performance

Active Publication Date: 2018-06-12
GEER TECH CO LTD
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Problems solved by technology

[0005] The present invention provides a method and device for pattern recognition to solve the problems of poor convergence effect and low recognition accuracy of kernel possibility fuzzy C-means clustering algorithm in the prior art

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Embodiment Construction

[0018] The technical idea of ​​the present invention is to propose a solution to the problems existing in the improved VQ algorithm, such as the KPFCM (Kernel Possibility Fuzzy C-Means) clustering algorithm. The inventors of the present application found that: although KPFCM has made great progress compared with the traditional method, the parameters of the kernel function of the KPFCM clustering algorithm are fixed, which cannot accurately reflect the discreteness of the feature data itself, and the convergence result is easy to fall into a local optimum Excellent, which limits the performance of clustering.

[0019] For this reason, for the parameter setting of the kernel function of the KPFCM algorithm is fixed, the distribution characteristics of the feature data itself cannot be fully utilized, and the clustering result is difficult to achieve the optimal defect. This embodiment proposes an adaptive kernel possibility fuzzy C-means aggregation Class algorithm (Adaptive Ke...

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Abstract

The invention discloses a pattern recognition method and device. The pattern recognition method includes the steps of carrying out feature extraction on input training data; after clustering analysisis conducted through an adaptive kernel possibility fuzzy C-means clustering algorithm, creating a reference model and saving the reference model into a reference model database; conducting same feature extraction on input testing data to obtain a testing feature vector, and executing pattern matching on the testing feature vector and each reference model in the reference model database to obtaina pattern recognition result. The pattern recognition device includes a training unit and a recognition unit. According to the scheme of the embodiment, the reference model is created after the clustering analysis is carried out through the adaptive kernel possibility fuzzy C-means clustering algorithm (AKPFCM), adaptive adjustment of the parameter of a kernel function can be achieved according tothe distribution condition of the feature data, better clustering of the feature data is achieved, and the clustering performance is effectively improved.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a pattern recognition method and device. Background technique [0002] With the development of information technology, pattern recognition has more and more extensive demands. After extracting the features of the signal, it needs to be recognized reliably through a suitable pattern matching method. Commonly used pattern recognition methods mainly include: template matching, vector quantization (Vector Quantization, referred to as VQ), probability statistics model method, artificial neural network and support vector machine (Support Vector Machine, referred to as SVM). Among them, VQ forms a codebook that can characterize the pattern by performing cluster analysis on the feature vectors extracted from the signal to be identified. Cluster analysis is an important method in unsupervised pattern recognition, which has been widely used in data mining, image processing, a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 苏鹏程张一凡
Owner GEER TECH CO LTD
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