Ship Rolling Prediction Based on Gray RBF Neural Network

2011 ◽  
Vol 48-49 ◽  
pp. 1044-1048 ◽  
Author(s):  
Li Sang Liu ◽  
Xia Fu Peng ◽  
Jie Hua Zhou
Keyword(s):  
Neural Network ◽  
Prediction Error ◽  
Rbf Neural Network ◽  
Second Order ◽  
Prediction Algorithm ◽  
Roll Motion ◽  
Gray Model ◽  
Rbf Network ◽  
Ship Roll ◽  
Simulation Results

To enhance the ship’s seaworthiness and seakeeping capacity, a new prediction algorithm based on Gray RBF neural network is presented to forecast roll motion accurately. The second-order gray model GM(2,1) and RBF network are introduced firstly, then using AGO (accumulated generating operation) to weaken randomness and volatility of raw data, which would affect the accuracy of RBF network. On the other hand, the algorithm flow of GMRBF(2,1) is given. Further more, GMRBF(2,1) is applied in a sample of ship roll sequence and effectively improves large prediction error of second-order gray model. The simulation results prove that the new model is more accurate and stabilizer than traditional models.

Garbage Crusher Fault Diagnosis Based on RBF Neural Network

2009 ◽  
Vol 16-19 ◽  
pp. 971-975
Author(s):  
Yong Hou Sun ◽  
Cong Li ◽  
Mei Fa Huang ◽  
Hui Jing
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Structure And Properties ◽  
Automatic Equipment ◽  
Rbf Network ◽  
The Neural Network ◽  
Diagnosis Model ◽  
Simulation Results ◽  
Training Simulation

The garbage crusher is a new kind of crusher for garbage crushing when processing Municipal Solid Waste (MSW). With the development of automatic equipment and the complication of structure and properties of the garbage crusher, the fault diagnosis of garbage crusher is very important. In this paper, according to the fault symptoms and parameters, Radial Basis Function Neural Network (RBF NN) is used for fault diagnosis of the garbage crusher. The structure and inference of RBF NN are discussed in detail. The garbage crusher fault diagnosis model is established based on RBF network. At last, the fault of mechanical system is taken as an example of garbage crusher fault diagnosis. Training simulation results of the neural network are given base on MATLAB software. The result shows the RBF NN is suitable for fault diagnosis of garbage crusher.

Research on Intelligent Diagnosis Technology of Transformer Fault

2013 ◽  
Vol 385-386 ◽  
pp. 589-592
Author(s):  
Hong Qi Wu ◽  
Xiao Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Intelligent Diagnosis ◽  
Rbf Network ◽  
Working Principle ◽  
Research On Application ◽  
Transformer Fault

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

Application of BP Neural Network into the Kow of Chemical Contaminants

2010 ◽  
Vol 121-122 ◽  
pp. 574-578
Author(s):  
Hui Yu Jiang ◽  
Min Dong ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Partition Coefficient ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Rbf Neural Network ◽  
Physical Parameters ◽  
Bp Network ◽  
Chemical Contaminants ◽  
Water Partition Coefficient ◽  
Molecular Bond

The octanol / water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol / water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the BP network to predict, the correlation coefficient has achieved 0.998, the prediction error in the permission scope.

Fault Diagnosis Analysis of Rotor System Based on RBF Neural Network and Dynamic Systems

2012 ◽  
Vol 460 ◽  
pp. 127-130
Author(s):  
Song He Zhang ◽  
Yue Gang Luo ◽  
Bin Wu ◽  
Bing Cheng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Amplitude Spectrum ◽  
Rates Of Convergence ◽  
Rotor System ◽  
Rbf Network ◽  
Characteristic Quantity ◽  
Learning Rates ◽  
Fault Characteristic

The RBF network was applied in the rotor system to realize the fault diagnosis aiming the mapping complexity between fault symptoms and fault patterns. It can overcome the problems of low learning rates of convergence and falling easily into part minimums in BP algorithm, and improve the precision of diagnosis. The normalized values of seven frequency ranges in amplitude spectrum were used as the fault characteristic quantity, the RBF network was trained to diagnose the faults of rotor system. The results show that RBF neural network is a valid method of diagnosis of mechanical failure.

Prediction on China's Energy Demand Based on RBF Neural Network Model

2013 ◽  
Vol 805-806 ◽  
pp. 1421-1424
Author(s):  
Xue Feng ◽  
Wuyunbilige Bao ◽  
Ben Ha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Neural Network Model ◽  
Energy Demand ◽  
Rbf Neural Network ◽  
Bp Network ◽  
Factors Affecting ◽  
Rbf Network ◽  
Network Prediction ◽  
Main Factors

Choose factors which influence the energy demand by the method of path analysis, build radial basis function (RBF) neural network model to predict energy demand in China. The RBF neural network is trained with the actual data of the main factors affecting energy demand during 1989-2003 and energy demand during 1993-2007 as learning sample with a good fitting effect. After testing network with the actual data of the main factors affecting energy demand during 2004-2007 and energy demand during 2008-2011, higher prediction accuracy can be obtained. By comparison with the BP network, RBF network prediction model outperforms BP network prediction model, finally RBF network is applied to make prediction of energy consumption for the year 2013-2015.

The Application of RBF Neural Networks in Curve Fitting

2012 ◽  
Vol 490-495 ◽  
pp. 688-692
Author(s):  
Zhong Biao Sheng ◽  
Xiao Rong Tong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Rbf Neural Network ◽  
Engineering Application ◽  
Nonlinear Function ◽  
Rbf Neural Networks ◽  
Neural Network Approach ◽  
Rbf Network ◽  
Fast Learning ◽  
Nonlinear Processing

Three means to realize function approach such as the interpolation approach, fitting approach as well as the neural network approach are discussed based on Matlab to meet the demand of data processing in engineering application. Based on basic principle of introduction, realization methods to non-linear are researched using interpolation function and fitting function in Matlab with example. It mainly studies the RBF neural networks and the training method. RBF neural network to proximate nonlinear function is designed and the desired effect is achieved through the training and simulation of network. As is shown from the simulation results, RBF network has strong nonlinear processing and approximating features, and RBF network model has the characteristics of high precision, fast learning speed for the prediction.

Seismic Damage Prediction of Multistory Brick Buildings Based on RBF Neural Network Model

2010 ◽  
Vol 163-167 ◽  
pp. 4213-4217
Author(s):  
Ling Wang ◽  
Li Sun ◽  
Dan Dan Kong ◽  
Xi Yuan Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Structure ◽  
Rbf Neural Network ◽  
Seismic Damage ◽  
Analytic Method ◽  
Disaster Prevention ◽  
Damage Prediction ◽  
Rbf Network

Seismic damage prediction of multistory brick buildings is a multi-factor nonlinear complex problems, this paper analyzed the deficiencies of the traditional methods for predicting the seismic damage, so a prediction model of multistory brick buildings based on RBF Neural Network model is established, RBF network structure elements and parameters are studied and obtained. With examples the research proved that the prediction results are similar to the actual seismic damage to multistory brick buildings by the RBP neural network model, the analytic method and process discussed in this paper can also be applied to seismic damage prediction of brick buildings of urban earthquake disaster prevention planning.

Study on Structural Damage Detection Using RBF Network

2014 ◽  
Vol 578-579 ◽  
pp. 1125-1128
Author(s):  
Jin Sheng Fan ◽  
Ying Yuan ◽  
Xiu Ling Cao
Keyword(s):  
Neural Network ◽  
Mode Shape ◽  
Structural Damage ◽  
Rbf Neural Network ◽  
Frame Structure ◽  
Structural Damage Detection ◽  
Element Analysis ◽  
Rbf Network ◽  
Damage Location ◽  
Trained Neural Network

Based on mode shape, a new parameter was put forward—mode shape curvature ratio, for detecting structure damages. And it was also the input vector of the RBF neural network. Then through finite element analysis and calculating, the training and forecasting samples were got for the network. The trained neural network can identify the damage location and degree of the frame structure. It proved that this method is simple and valid.

scholarly journals A Novel Positioning Algorithm Based on Self-adaptive Algorithm of RBF Network

2016 ◽  
Vol 10 (1) ◽  
pp. 141-148 ◽  
Author(s):  
Jin Ren ◽  
Jingxing Chen ◽  
Liang Feng
Keyword(s):  
Neural Network ◽  
Series Expansion ◽  
Taylor Series ◽  
Lower Cost ◽  
Rbf Neural Network ◽  
Taylor Series Expansion ◽  
Rbf Network ◽  
Location Algorithm ◽  
Positioning Algorithm ◽  
Self Adaptive

Much attention has been paid to Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning. A Taylor-series expansion location algorithm based on the RBF neural network (RBF-TSE) is proposed before to the performance of TSE highly depends on the initial estimation. In order to have more accurate and lower cost,a new Taylor-series expansion location algorithm based on Self-adaptive RBF neural network (SA-RBF-TSE) is proposed to estimate the initial value. The proposed algorithm is analysed and simulated with several other algorithms in this paper.

Wind Energy Resources Prediction Based on EOF Method and RBF Neural Network

2013 ◽  
Vol 281 ◽  
pp. 550-553
Author(s):  
Xiao Cao ◽  
Zhi Bao Chen ◽  
Hai Zhou ◽  
Jie Ding
Keyword(s):  
Neural Network ◽  
Spatial Patterns ◽  
Rbf Neural Network ◽  
Energy Resources ◽  
Orthogonal Function ◽  
Time Coefficient ◽  
Prediction Time ◽  
Simulation Results ◽  
Wind Energy Resources ◽  
Prediction Approach

In this paper, research starts from the data captured from several wind measuring stations. Firstly, the main spatial Patterns are extracted by EOF (empirical orthogonal function) method, and then the time coefficient series corresponding to principal spatial patterns are processed and predicted by RBF (radial basis function) neural network. Furthermore, according to the EOF decomposition method, inversely the new prediction time coefficient series are used to calculate the wind speed values in the future. Finally, the validity and advantages of this prediction approach are tested by the simulation results.

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