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Research and Implementation on JTIDS Signal User Sorting Classifier

Author: FangXiaoYing
Tutor: ZhangXiaoYi
School: PLA Information Engineering University
Course: Communication and Information System
Keywords: JTIDS Classifier Support Vector Machine Clustering Neural Network
CLC: TN911.7
Type: Master's thesis
Year: 2009
Downloads: 18
Quote: 0
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Abstract


In this paper, the JTIDS signal characteristics, support vector machines, Clustering and Neural Network are three commonly used classification algorithm the JTIDS signal the user selected application, analyze and compare the performance of the sorting. Based on this, the user class number of known and unknown both cases, research and design JTIDS signal separation classifier program and implements a software platform. Mainly to complete the work as follows: 1, research JTIDS users based on support vector machine classifier. Discussed in detail several deformation algorithm, support vector machine through analysis and comparison of its performance to determine the LS-SVM optimum classification algorithm based on the JTIDS user SVM classifier. Multi-user features, and combined with JTIDS signal LS-SVM SVM multi-class classification algorithm. 2 JTIDS user classification based on clustering. Actual situation, combined with JTIDS signal classification algorithm based on K-means for its shortcomings is not easy to determine the number of clusters and the initial cluster centers has been improved, the improved algorithm can adaptively determine the number of clusters and the initial poly class center to avoid the subjectivity and the initial number of clusters selected by blindness. Further, the SVC algorithm, a relatively good separation performance improved K-means classifier is determined by the analysis of several classification algorithm. 3 JTIDS users based on neural network classifier. The RBFN algorithm based on the analysis, the central location for the hidden layer node number, radial basis, not to determine the shortcomings of the width and the weight value of the hidden layer center given K-means RBFN combining algorithm - K -RBFN, the algorithm using the K-means to determine the parameters RBFN RBFN algorithm Further classification. SOFM algorithm explore, SOFM and LS-SVM's advantage, given the SOFM-SVM integration algorithm, and the effectiveness of the improved SD function to determine the number of clusters in the SOFM, it better be used in the subject, improved the classification performance has improved greatly. 4, on the basis of analysis of the performance of several classification, the number of categories of users known and unknown both cases were designed the JTIDS user sorting classifier program, the use of VC to build a software testing platform to verify the program The feasibility. Finally, a summary of the work carried out on this subject, deficiencies and improvements needed in the course of the study are described, and follow-up.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Communication theory > Signal processing
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