Fuzzy Clustering RBF Neural Network Applied to Signal Processing of the Imaging Detection

2010 ◽  
Author(s):  
Yongxue Wang ◽  
Yan Shang
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Fuzzy Clustering ◽  
Rbf Neural Network ◽  
Imaging Detection

scholarly journals Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network

2020 ◽  
Vol 213 ◽  
pp. 03002
Author(s):  
Guozhen Ma ◽  
Po Hu ◽  
Yunjia Wang ◽  
Yongli Wang ◽  
Chengcong Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Rbf Neural Network ◽  
Distribution Network ◽  
Load Forecasting ◽  
Current Load ◽  
Short Term ◽  
Forecast Period ◽  
Short Term Load Forecasting ◽  
Load Characteristics

In order to solve the diversification of the load characteristics of the distribution network due to the difference in the electric structure and the electricity consumption habits of users, the calculation accuracy of the forecast model is difficult to meet the actual demand. In this paper, through in-depth study of the characteristics of ultra-short-term load, an ultra-short-term load forecasting model based on fuzzy clustering and RBF neural network (FCM-RBF) is constructed. The model not only considers the historical load characteristics of locally similar days, but also considers the current load characteristics of the forecast days. The load on a locally similar day can well reflect the overall trend of the predicted load; the current load on the forecast day can well reflect the changing law of real-time data during the forecast period and some random factors in the forecast period. Finally, a power grid load in a certain area of southwestern China is selected as an example to verify the effectiveness and accuracy of the proposed method.

The Application of Alterable Parameter Genetic Algorithm and Neural Network in Artillery Locating Radar Detecting Small Signal

2013 ◽  
Vol 645 ◽  
pp. 259-262
Author(s):  
Shang Guo Tan ◽  
Rui Dong Hou ◽  
Wei Pan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Signal Processing ◽  
Signal Detection ◽  
Detection Probability ◽  
Frequency Estimation ◽  
Rbf Neural Network ◽  
Small Signal ◽  
Low Snr ◽  
Whitening Process

To perform effective radar small signal detection in low SNR, a signal-processing model is established. In the model, the feature factors that distinguish small signal from noise are defined with whitening process and feature decomposition frequency estimation, then the RBF parameters are optimized by using genetic algorithm and APGA-RBF neural network is formed to realize classification, thereby the small signal detection is completed. Results of simulation show that the detection probability is greatly increased as well as the performance of classification.

A New Training Algorithm for RBF Neural Network based on Dynamic Fuzzy Clustering

2012 ◽  
Vol 241-244 ◽  
pp. 1593-1597
Author(s):  
Yan Jun Cui ◽  
Yan Dong Ma ◽  
Jie Li ◽  
Zheng Zhao
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Traditional Method ◽  
Rbf Neural Network ◽  
Training Algorithm ◽  
Clustering Method ◽  
Fuzzy Clustering Method ◽  
Complex Nonlinear Systems ◽  
Hidden Nodes ◽  
Dynamic Fuzzy Clustering

A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper. This algorithm is based on the dynamic fuzzy clustering method (DFCM). The algorithm has a number of advantages compared to the traditional method based on k-means. For example, it does not need to know the number of the hidden nodes and to predicts more accurately. Due to these advantages, this method proves to be suitable for developing models for complex nonlinear systems.

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