Uncertain data stream algorithm based on clustering RBF neural network |
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| Affiliation: | 1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China;2. Research Center of Maritime Security Technology, China Waterborne Transport Research Institute, Beijing 100088, China;3. Institute of Quantitative and Technical Economics, Chinese Academy of Social Sciences, Beijing 100002, China;4. School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China;5. International School, Beijing University of Posts and Telecommunications, Beijing 100876, China;1. Prenatal Diagnosis Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, China;2. School of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, 637000, China;3. Obstetrics and Gynecology Department, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, China;2. The Department of Basic Education, Shanghai Urban Construction Vocational College, Shanghai, 201415, China;1. Pediatrics, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116023, China;2. Department of Rheumatology and Immunology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, 116023, China;1. Department of Electrical and Electronics Engineering, P.A. College of Engineering and Technology, Pollachi, India;2. Department of Electrical and Electronics Engineering, P.A. College of Engineering and Technology, Pollachi, India |
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| Abstract: | In this paper, the existing algorithms for modeling uncertain data streams based on radial basis function neural networks have problems of low accuracy, weak stability and slow speed. A new clustering method for uncertain data streams is proposed. Radial basis function neural network of the algorithm. The algorithm firstly models the uncertain data stream, then combines the fuzzy theory and the neural network principle to obtain the radial basis function neural network, and then obtains the radial basis function neural network through the clustering algorithm of the regular tetrahedral uncertain vector. The central weight and width weights ultimately result in hidden layer output and output layer output results. The experimental results show that the proposed algorithm is an effective algorithm for modeling uncertain data streams using clustering radial basis function neural networks. It has higher precision, stability and speed than similar algorithms. |
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