The Prediction of Grounding Grid Corrosion Rate Using Optimized RBF Network

2014 ◽  
Vol 596 ◽  
pp. 245-250
Author(s):  
Chun Feng Song ◽  
Yuan Bin Hou ◽  
Jing Yi Du
Keyword(s):  
Neural Network ◽  
Corrosion Rate ◽  
Ant Colony Algorithm ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Ant Colony ◽  
Rbf Network ◽  
Grounding Grid ◽  
Ant Colony Clustering ◽  
Hidden Layer

Because the grounding grid corrosion rate has the property of nonlinearity and uncertainty, it is very difficult for us to predict precisely. The approach is proposed that ant colony clustering algorithm is combined with RBF neural network to predict the grounding grid corrosion rate, using ant colony clustering algorithm to get the center of hidden layer neurons. To find the best clustering result, local search is applied in ant colony algorithm. This model has good performance of strong local generalization abilities and satisfying accuracy. At last, it is proved with lots of experiments that the application is fairly effective.

Application of Ant Colony Algorithm in Plant Leaves Classification Based on Infrared Spectroscopy Analysis

2014 ◽  
Vol 633 ◽  
pp. 503-506
Author(s):  
Mei Lin Gao Sun ◽  
Ping Wu ◽  
Kai Li ◽  
Zhen Hua Gen
Keyword(s):  
Infrared Spectroscopy ◽  
Acer Saccharum ◽  
Ant Colony Algorithm ◽  
Clustering Algorithm ◽  
Near Infrared ◽  
Ant Colony ◽  
Plant Leaves ◽  
Process Data ◽  
Spectroscopy Analysis ◽  
Ant Colony Clustering

Intelligent classification is realized according to different components of featured information included in near infrared spectrum data of plants. The core of this theory is to research applications of ant colony algorithm in spectral analysis of plant leaves through theories and experiments. In aspect of theoretical exploration, the built-in function of clustering algorithm is used to compress and process data. In aspect of experimental research, the near infrared diffuse emission spectrum curves of the leaves of Cinnamomum camphora and Acer saccharum Marsh in two groups, which have 75 leaves respectively. Then, the obtained data are processed using ant colony algorithm and the same leaves can be classified as a class by ant colony clustering algorithm. Finally, the two groups of data are classified into two classes. Our results show the distinguishability can be 100%. Keywords:Near infrared spectroscopy; ant colony algorithm; clustering algorithm; signal processing

Noisy Speech Recognition Based on RBF Neural Network

2011 ◽  
Vol 271-273 ◽  
pp. 597-602
Author(s):  
Gang Yan ◽  
Hai Dong Kong ◽  
Yang Yu ◽  
Xiao Xia Zheng
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Training Methods ◽  
Noisy Speech ◽  
Rbf Network ◽  
Final Prediction Error ◽  
Noisy Speech Recognition ◽  
Hidden Layer

A noisy speech recognition method based on improved RBF neural network is presented, which the parameters of hidden layer are trained dynamically, and Akaike’s final prediction error standard (FPE) is employed to simplify the network. Comparing with two other training methods of RBF network, experimental results based on noisy speech samples show that this method achieves excellent performance in terms of recognition rate and recognition speed.

Noise-Robust Speech Recognition Based on RBF Neural Network

2011 ◽  
Vol 217-218 ◽  
pp. 413-418
Author(s):  
Xue Mei Hou
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Recognition System ◽  
Robust Speech Recognition ◽  
Rbf Network ◽  
Noise Robust Speech Recognition ◽  
Noise Robust

Considering the actuality of current speech recognition and the characteristic of RBF neural network, a noise-robust speech recognition system based on RBF neural network is proposed with the entire-supervised algorithm. If the traditional clustering algorithm is employed, there is a flaw that the node center of hidden layer is always sensitive to the initial value, but if the entire-supervised algorithm is used, the flaw will not turn up, and the classification ability of RBF network will be enhanced. Experimental results show that, compared with the traditional clustering algorithm, the entire-supervised algorithm is of higher recognition rate in different SNRs than that of clustering algorithm.

The RBF Neural Network Based on Kalman Filter Algorithm and Dual Radial Transfer Function

2014 ◽  
Vol 971-973 ◽  
pp. 1816-1819 ◽  
Author(s):  
Shao Yun Song ◽  
Bao Hua Zhang ◽  
Yu Ma
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Radial Function ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Network Resource ◽  
Rbf Network ◽  
Kalman Filter Algorithm ◽  
The Common ◽  
Hidden Layer

RBF neural network have advantages of training simple, fast efficiency of learning, easy to fall into local minima, etc..It is widely used to solve the problem in signal processing and pattern recognition. Although the common RBF network is relatively easy to build, but because of the structure is usually fixed or high complexity, resulting in learning time is too long or network resource waste. For these reasons, proposed using extended Kalman filter as the RBF learning algorithm, and using double radial function in the hidden layer. By approaching the basis of the results of the analysis clearly shows that the network model than the other categories have a stronger generalization.

Design and Implementation of Airline Customer Segmentation System Based on Ant Colony Clustering Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 3357-3361
Author(s):  
Li Feng Wei
Keyword(s):  
Customer Value ◽  
Ant Colony Algorithm ◽  
Clustering Algorithm ◽  
Data Representation ◽  
Customer Segmentation ◽  
Ant Colony ◽  
Customer Relationship ◽  
System Structure ◽  
Ant Colony Clustering ◽  
Analysis Platform

Segmentation based on customer value and needs can better guide marketing decision-making of airlines as well as better understand needs of high-value passengers. To address customer segmentation in Customer Relationship Management (CRM), the paper proposed and designed airline customer segmentation system structure based on ant colony clustering. The key ant colony clustering algorithm was also designed and implemented. The ant colony clustering algorithm mainly used adaptively adjusted group similarity to perform clustering and access to initial clustering result. Then all data representation points and abnormal data were inputted into lattice plane scattered randomly. Ant colony algorithm was used for clustering once again and corresponding class label was used to delete abnormal values and obtain complete clusters. Data test example based on ant colony clustering customer analysis platform illustrated its feasibility and effectiveness

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