A nonlinear prediction model for Chinese speech signal based on RBF neural network

2022 ◽  
Author(s):  
Xiaohong Gao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Speech Signal ◽  
Rbf Neural Network ◽  
Nonlinear Prediction

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 Research Based on RBF Neural Network in the Power of Prediction of Grain Depot

2012 ◽  
Vol 182-183 ◽  
pp. 1358-1361
Author(s):  
Le Xiao ◽  
Min Peng Hu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Electricity Consumption ◽  
Nonlinear Time Series ◽  
Modeling And Prediction ◽  
Radial Basis

According to the fact that the use of electricity in grain depot is nonlinear time series, the article introduces the prediction model of electricity based on Radial Basis Function Neural Network, and conducts the modeling and prediction by adopting the historical electricity consumption of a typical grain depot. As the result of simulation shows, the model obtains better forecasting results in grain depot electricity.

scholarly journals Experimental Test and Prediction Model of Soil Thermal Conductivity in Permafrost Regions

2020 ◽  
Vol 10 (7) ◽  
pp. 2476 ◽  
Author(s):  
Fu-Qing Cui ◽  
Zhi-Yun Liu ◽  
Jian-Bing Chen ◽  
Yuan-Hong Dong ◽  
Long Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Prediction Model ◽  
Rbf Neural Network ◽  
Soil Thermal Conductivity ◽  
Engineering Applications ◽  
Frozen Soils ◽  
Fitting Method ◽  
Network Method ◽  
Permafrost Regions

Soil thermal conductivity is a dominant parameter of an unsteady heat-transfer process, which further influences the stability and sustainability of engineering applications in permafrost regions. In this work, a laboratory test for massive specimens is performed to reveal the distribution characteristics and the parameter-influencing mechanisms of soil thermal conductivity along the Qinghai–Tibet Engineering Corridor (QTEC). Based on the measurement data of 638 unfrozen and 860 frozen soil specimens, binary fitting, radial basis function (RBF) neural network and ternary fitting (for frozen soils) prediction models of soil thermal conductivity have been developed and compared. The results demonstrate that, (1) particle size and intrinsic heat-conducting capacity of the soil skeleton have a significant influence on the soil thermal conductivity, and the typical specimens in the QTEC can be classified as three clusters according to their thermal conductivity probability distribution and water-holding capacity; (2) dry density as well as water content sometimes does not have a strong positive correlation with thermal conductivity of natural soil samples, especially for multiple soil types and complex compositions; (3) both the RBF neural network method and ternary fitting method have favorable prediction accuracy and a wide application range. The maximum determination coefficient (R2) and quantitative proportion of relative error within ±10% ( P ± 10 % ) of each prediction model reaches up to 0.82, 0.88, 81.4% and 74.5%, respectively. Furthermore, because the ternary fitting method can only be used for frozen soils, the RBF neural network method is considered the optimal approach among all three prediction methods. This study can contribute to the construction and maintenance of engineering applications in permafrost regions.

RPSOVF Prediction Model for Speech Signal Series Based on UPSO

2019 ◽  
Vol 29 (06) ◽  
pp. 1950075
Author(s):  
Yumei Zhang ◽  
Xiangying Guo ◽  
Xia Wu ◽  
Suzhen Shi ◽  
Xiaojun Wu
Keyword(s):  
Prediction Model ◽  
Speech Signal ◽  
Signal Reconstruction ◽  
Volterra Model ◽  
Speech Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Nonlinear Prediction ◽  
Absolute Deviation ◽  
Low Performance

In this paper, we propose a nonlinear prediction model of speech signal series with an explicit structure. In order to overcome some intrinsic shortcomings, such as traps at the local minimum, improper selection of parameters, and slow convergence rate, which are always caused by improper parameters generated by, typically, the low performance of least mean square (LMS) in updating kernel coefficients of the Volterra model, a uniform searching particle swarm optimization (UPSO) algorithm to optimize the kernel coefficients of the Volterra model is proposed. The second-order Volterra filter (SOVF) speech prediction model based on UPSO is established by using English phonemes, words, and phrases. In order to reduce the complexity of the model, given a user-designed tolerance of errors, we extract the reduced parameter of SOVF (RPSOVF) for acceleration. The experimental results show that in the tasks of single-frame and multiframe speech signals, both UPSO-SOVF and UPSO-RPSOVF are better than LMS-SOVF and PSO-SOVF in terms of root mean square error (RMSE) and mean absolute deviation (MAD). UPSO-SOVF and UPSO-RPSOVF can better reflect trends and regularity of speech signals, which can fully meet the requirements of speech signal prediction. The proposed model presents a nonlinear analysis and valuable model structure for speech signal series, and can be further employed in speech signal reconstruction or compression coding.

Research on Predictive Model of Gas Concentration for Driving Ventilation in Coal Mine Based on RBF and BP

2013 ◽  
Vol 706-708 ◽  
pp. 1805-1809
Author(s):  
Xiao Yan Gong ◽  
Jun Guo ◽  
He Xue ◽  
Dong Hui Yan ◽  
Zhe Wu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Rbf Neural Network ◽  
Coal Mines ◽  
Observation Data ◽  
Prediction Errors ◽  
Application Research ◽  
Gas Concentration ◽  
Mine Sites

In order to predict accurately gas concentration and design ventilation scheme in driving ventilation process under different gas emission in coal mine, based on the analysis of various ventilation factors, the prediction model structure of gas concentration for driving ventilation was designed based on RBF and BP neural network in this paper. Then MATLAB software and the observation data obtained from the coal mine sites were used to compare and analyze the prediction errors of two models, and a RBF neural network model with higher prediction precision was obtained. After that, the prediction model was used for practical application research on the gas concentration of the heading face in concrete coal mines. The research shows that the settled prediction model can not only predict the gas concentration precisely of driving ventilation, but also provide a certain theory basis for different driving ventilation equipment layout and parameters configuration in the driving ventilation process of coal mines.

The Analysis of Combined Prediction Model of Building Energy Consumption with Grey Theory and RBF Neural Network

2011 ◽  
Vol 374-377 ◽  
pp. 90-93
Author(s):  
Yan Bai ◽  
Qing Chang Ren ◽  
Hong Mei Jiang
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Rbf Neural Network ◽  
Grey Theory ◽  
The Novel ◽  
Grey Prediction ◽  
Consumption Data ◽  
Building Area ◽  
Simulation And Experiment

A kind of new combined modeling method with GM(1,1) and RBNN (Radial Basis Neural Network) is brought forward, according to the idea that the method of neural network can bring grey prediction model a good modified effect. Based on the analysis of the energy consumption data of the existing and the annually-increased building area, the GM(1,1) model was then constructed. And the RBF neural network was used for the model residual error revising. The simulation and experiment results show that the novel model is more effective than the common grey model.

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