Traffic Forecast Based on Empirical Mode Decomposition and RBF Neural Network

2013 ◽  
Vol 846-847 ◽  
pp. 1270-1273
Author(s):  
Shi Bao Jiang ◽  
Chuan Feng Bai ◽  
Cui Feng Du
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mobile Networks ◽  
Rbf Neural Network ◽  
Base Stations ◽  
Scale Analysis ◽  
Network Coverage ◽  
Traffic Jam ◽  
Multi Scale ◽  
Mode Decomposition

Traffic analysis and forecast are the classic topics of telecommunication research, as they provide strategic ground to address the issues such as mobile networks traffic jam, network coverage planning design, marketing management etc. On the basis of the empirical mode decomposition theory and methods, this article first implements multi-scale analysis of the time series of traffic, then it goes on to executes RBF neural network based on the different compositions, and finally reach the forecast expectation of voice traffics of selected base stations.

scholarly journals The Analysis of China’s Grain Output Fluctuation Based on EMD

2017 ◽  
Vol 9 (11) ◽  
pp. 64
Author(s):  
Qiting Chen ◽  
Meng Wang
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Driving Forces ◽  
Unit Area ◽  
Rbf Neural Network ◽  
The Other ◽  
Cyclical Fluctuation ◽  
Output Fluctuations ◽  
Mode Decomposition ◽  
Emd Method

Food is one of the most important resources for staying alive. This paper analyzes grain output fluctuations and their driving forces in China from 1978 to 2014, based on Empirical Mode Decomposition (EMD) method. These results show that there are two type cycles of cyclical fluctuation, one is 3-yearterm, and another is 8-year term. These results show that the 8-year cyclical fluctuation is the major term. Grain production’s cyclical fluctuation in 3 years was mainly influenced by yield of grain per unit area from 1978-2004 and 2007-2014, and by the area sown from 2004 to 2007. On the other hand, the longer cyclical fluctuation of 8 years is mainly affected by the yield of grain per unit area. The grain output is predicted for the next three years through the RBF neural network optimized by PSO. These results show that China’s annul grain output in the next three years will be stabilized at about 600 million tons, which may grow slowly though.

Speech Recognition Algorithm Based on Empirical Mode Decomposition and RBF Neural Network

2013 ◽  
Vol 831 ◽  
pp. 465-469
Author(s):  
Wei Wei Shi ◽  
Wei Hua Xiong ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Empirical Mode Decomposition ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Good Method ◽  
Recognition Algorithm ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Learning Speed

This paper presents a novel method of the speech recognition in combining the empirical mode decomposition with radical basis function neural network. Speech signals which pretreated are decomposed by empirical mode decomposition to get a set of intrinsic mode functions. It extracts mel frequency cepstrum coefficient from intrinsic mode function. Features parameters are made up of the coefficients. For BP Neural Network, RBF Neural Network has advantages on approximating ability and learning speed. So using RBF Neural Network as a recognition model is a good method. Experiments show that this new method has good robustness and adaptability. The speech recognition rate of this method reach ninety-one percents accurately under no noise environment. Speech signal recognition is feasible and effective in noisy environment.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

Sign in / Sign up

Export Citation Format

Share Document