A New Algorithm for Long-Term Estimation Based on AR Model

2014 ◽  
Vol 614 ◽  
pp. 440-443 ◽  
Author(s):  
Wen Jun Su ◽  
Hai Tao Chen
Keyword(s):  
Spectral Density ◽  
Poor Performance ◽  
Ar Model ◽  
Estimation Methods ◽  
Wiener Model ◽  
Training Set ◽  
Power Spectral ◽  
Cross Spectral Density ◽  
Term Data

Traditional estimation methods have poor performance for long-term data forecast. Using Wiener model to estimate, power spectral density of the input signal, and cross-spectral density of the input and output signals are needed, that are difficult to obtain. And the large amount of calculation is needed using Wiener model. Using AR model and Kalman model, estimated results tend to mean of the training set while the estimated distance increases. For these cases, a new algorithm for long-term estimation based on AR model, named sampling AR model, is presented. Grouping the training set and using a different group of the training set to estimate each value. Sampling AR model improves the accuracy of long-term estimation.

A New Random Data Description and Its Use in Transferring Road Roughness to Vehicle Response

1974 ◽  
Vol 96 (2) ◽  
pp. 676-679 ◽  
Author(s):  
J. C. Wambold ◽  
W. H. Park ◽  
R. G. Vashlishan
Keyword(s):  
Spectral Density ◽  
Frequency Distribution ◽  
Descriptive Analysis ◽  
Joint Probability ◽  
Amplitude Distribution ◽  
Matrix Functions ◽  
Initial Portion ◽  
Power Spectral ◽  
Road Roughness ◽  
Cross Spectral Density

The initial portion of the paper discusses the more conventional method of obtaining a vehicle transfer function where phase and magnitude are determined by dividing the cross spectral density of the input/output by the power spectral density (PSD) of the input. The authors needed a more descriptive analysis (over PSD) and developed a new signal description called Amplitude Frequency Distribution (AFD); a discrete joint probability of amplitude and frequency with the advantage of retaining amplitude distribution as well as frequency distribution. A better understanding was obtained, and transfer matrix functions were developed using AFD.

scholarly journals Enhancing the quality of communication of cellular networks using big data applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mozamel Musa Saeed ◽  
Mohammed Alsharidah
Keyword(s):  
Big Data ◽  
Spectral Density ◽  
Cellular Networks ◽  
Power Spectral Density ◽  
Spectral Estimation ◽  
Estimation Methods ◽  
Spectrum Estimation ◽  
Spectral Densities ◽  
Power Spectral ◽  
Welch Method

AbstractBoth software-defined networking and big data have gained approval and preferences from both industry and academia. These two important realms have conventionally been addressed independently in wireless cellular networks. The discussion taken into consideration in this study was to analyze the wireless cellular technologies with the contrast of efficient and enhanced spectral densities at a reduced cost. To accomplish the goal of this study, Welch's method has been used as the core subject. With the aid of previous research and classical techniques, this study has identified that the spectral densities can be enhanced at reduced costs with the help of the power spectral estimation methods. The Welch method gives the result on power spectrum estimation. By reducing the effect of noise, the Welch method is used to calculate the power spectral density of a signal. When data length is increased, Welch's method is considered the best as a conclusion to this paper because excellent results are yielded by it in the area of power spectral density estimation.

scholarly journals Stochastic bursting in networks of excitable units with delayed coupling

2021 ◽  
Author(s):  
Chunming Zheng ◽  
Arkady Pikovsky
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Directed Tree ◽  
Basic Assumptions ◽  
Delayed Coupling ◽  
Power Spectral ◽  
Delay Feedbacks ◽  
Cross Spectral Density ◽  
Tree Lattice ◽  
The One

AbstractWe investigate the phenomenon of stochastic bursting in a noisy excitable unit with multiple weak delay feedbacks, by virtue of a directed tree lattice model. We find statistical properties of the appearing sequence of spikes and expressions for the power spectral density. This simple model is extended to a network of three units with delayed coupling of a star type. We find the power spectral density of each unit and the cross-spectral density between any two units. The basic assumptions behind the analytical approach are the separation of timescales, allowing for a description of the spike train as a point process, and weakness of coupling, allowing for a representation of the action of overlapped spikes via the sum of the one-spike excitation probabilities.

Influence of Estimation Methods of Power Spectral Density Function on the Calculated Fatigue Life with Spectral Method

2014 ◽  
Vol 224 ◽  
pp. 118-123 ◽  
Author(s):  
Adam Niesłony ◽  
Michał Böhm ◽  
Agnieszka Materac
Keyword(s):  
Fatigue Life ◽  
Spectral Density ◽  
Spectral Method ◽  
Power Spectral Density ◽  
Probability Distributions ◽  
Spectral Density Function ◽  
Estimation Methods ◽  
Fundamental Parameters ◽  
Power Spectral ◽  
Matlab Package

Spectral method is one of the most effective methods of fatigue life estimation. In this method fundamental parameters characterizing the load process are determined on the basis of Power Spectral Density (PSD) function. PSD function can be obtained in many ways. Several functions from parametric methods, nonparametric methods and subspace method from Matlab package were discussed in this paper in terms of their use for the determination of fatigue life. Finally, calculated fatigue life was determined using spectral method and cycle counting method and compared using probability distributions of amplitudes according to Dirlik and Miner rule for damage summation.

Improved Methods for the Fast Fourier Transform (FFT) Calculation of the Frequency Response Function

1982 ◽  
Vol 104 (2) ◽  
pp. 277-279 ◽  
Author(s):  
L. D. Mitchell
Keyword(s):  
Spectral Density ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Coherence Function ◽  
Input And Output ◽  
At Resonance ◽  
Power Spectral ◽  
Cross Spectral Density ◽  
Improved Methods

Most FFT machines today compute an estimate of the frequency response function, H(f), by the cross-spectral density of input to output divided by the power spectral density of the input. This estimator is contaminated by noise at the input. One uses the coherence function to help measure the level of contamination. However, the coherence function detects, among other things, noise at both the input and output. Several alternate methods are proposed for the computation of the frequency response function. One generates more accurate estimates at resonance, one has half or less of the contamination contained in the present methods, and the last one proposes to eliminate the biasing contamination all together.

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