Identification of time-varying system with wavelet based approach using multiple wavelet basis functions

2015 ◽  
Author(s):  
Sandesh N. Mate
Keyword(s):  
Basis Functions ◽  
Wavelet Basis ◽  
Time Varying

Wavelets in the Solution of Nongray Radiative Heat Transfer Equation

1998 ◽  
Vol 120 (1) ◽  
pp. 133-139 ◽  
Author(s):  
Y. Bayazitoglu ◽  
B. Y. Wang
Keyword(s):  
Transfer Equation ◽  
Solution Method ◽  
Radiative Heat ◽  
Radiative Transfer Equation ◽  
Basis Functions ◽  
Wavelet Basis ◽  
System Of Equations ◽  
Heat Transfer Equation ◽  
One Dimensional ◽  
The One

The wavelet basis functions are introduced into the radiative transfer equation in the frequency domain. The intensity of radiation is expanded in terms of Daubechies’ wrapped-around wavelet functions. It is shown that the wavelet basis approach to modeling nongrayness can be incorporated into any solution method for the equation of transfer. In this paper the resulting system of equations is solved for the one-dimensional radiative equilibrium problem using the P-N approximation.

Discrete signal matching using coarse-to-fine wavelet basis functions

2003 ◽  
Vol 36 (1) ◽  
pp. 171-192 ◽  
Author(s):  
Yu.-Te. Wu ◽  
Li-Fen Chen ◽  
Po-Lei Lee ◽  
Tzu-Chen Yeh ◽  
Jen-Chuen Hsieh
Keyword(s):  
Basis Functions ◽  
Wavelet Basis ◽  
Discrete Signal ◽  
Coarse To Fine

Identification of Time-Varying Time Constants of Thermocouple Sensors and Its Application to Temperature Measurement

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Kenneth Kar ◽  
Akshya K. Swain ◽  
Robert Raine
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Time Constant ◽  
Identification Problem ◽  
Basis Functions ◽  
New Technique ◽  
Ratio Test ◽  
Time Varying ◽  
Time Constants ◽  
Orthogonal Least Squares

The present study addresses the problem of estimating time-varying time constants associated with thermocouple sensors by a set of basis functions. By expanding each time-varying time constant onto a finite set of basis sequences, the time-varying identification problem reduces to a parameter estimation problem of a time-invariant system. The proposed algorithm, to be called as orthogonal least-squares with basis function expansion algorithm, combines the orthogonal least-squares algorithm with an error reduction ratio test to include significant basis functions into the model, which results in a parsimonious model structure. The performance of the method was compared with a linear Kalman filter. Simulations on engine data have demonstrated that the proposed method performs satisfactorily and is better than the Kalman filter. The new technique has been applied in a Stirling cycle compressor. The sinusoidal variations in time constant are tracked properly using the new technique, but the linear Kalman filter fails to do so. Both model validation and thermodynamic laws confirm that the new technique gives unbiased estimates and that the assumed thermocouple model is adequate.

Ozone Prediction Performance of HMM Based on the Data Compressed by Different Wavelet Basis Functions

2012 ◽  
Vol 518-523 ◽  
pp. 1586-1591
Author(s):  
Hao Zhang ◽  
Ze Meng Zhao ◽  
Ahmet Palazoglu ◽  
Wei Sun
Keyword(s):  
Information Content ◽  
Shannon Entropy ◽  
Air Pollutants ◽  
Surface Ozone ◽  
Prediction Performance ◽  
Basis Functions ◽  
Wavelet Basis ◽  
Human Beings ◽  
Ozone Prediction ◽  
Geometric Ratio

Surface ozone in the air boundary layer is one of the most harmful air pollutants produced by photochemical reaction between nitrogen oxides and volatile hydrocarbons, which causes great damage to human beings and environment. The prediction of surface ozone levels plays an important role in the control and the reduction of air pollutants. As model-driven statistical prediction models, hidden Markov Models (HMMs) are rich in mathematical structure and work well in many important applications. Due to the complex structure of HMM, long observation sequences would increase computational load by geometric ratio. In order to reduce training time, wavelet decomposition is used to compress the original observations into shorter ones. During compression step, observation sequences compressed by different wavelet basis functions keep different information content. This may have impact on prediction results. In this paper, ozone prediction performance of HMM based on different wavelet basis functions are discussed. Shannon entropy is employed to measure how much information content is kept in the new sequence compared to the original one. Data from Houston Metropolitan Area, TX are used in this paper. Results show that wavelet basis functions used in data compression step can affect the HMM model performance significantly. The new sequence with the maximum Shannon entropy generates the best prediction result.

A Robust Time-Varying Identification Algorithm Using Basis Functions

2003 ◽  
Vol 31 (7) ◽  
pp. 840-853 ◽  
Author(s):  
Rui Zou ◽  
Hengliang Wang ◽  
Ki H. Chon
Keyword(s):  
Basis Functions ◽  
Identification Algorithm ◽  
Time Varying

Using Improved Wavelet Networks in Image Compression

2012 ◽  
Vol 239-240 ◽  
pp. 1018-1021
Author(s):  
Wei Wei Xiao ◽  
Wei Jun Luan
Keyword(s):  
Image Compression ◽  
Activation Function ◽  
Basis Functions ◽  
Wavelet Basis ◽  
Wavelet Network ◽  
Wavelet Networks ◽  
Mexican Hat ◽  
Better Than

We proposed improved wavelet network and applied it to image compression. Activation function was selected automatically from three wavelet basis functions based on the effect of image compression. The three given wavelet basis functions were Morlet, Mexican-Hat and Different Of Gauss(DOG) wavelet basis functions respectively. The results show that the improved wavelet networks method is better than general wavelet networks in image compression.

scholarly journals Determination of the Optimum Wavelet Basis Function for Indonesian Vowel Voice Recognition

2017 ◽  
Vol 17 (2) ◽  
pp. 42 ◽  
Author(s):  
Syahroni Hidayat ◽  
Habib Ratu P. N. ◽  
Danang Tejo Kumoro
Keyword(s):  
Cross Correlation ◽  
Recognition System ◽  
Basis Functions ◽  
Wavelet Basis ◽  
Trial And Error ◽  
Wavelet Base ◽  
Automatic Speech Recognition System ◽  
Base Function ◽  
Wavelet Family

Nowadays, wavelet has been widely applied in extracting features of the signal for automatic speech recognition system. Wavelets have many families that are determined by their mother function and order. The use of different wavelets to analyze the same signal would bring different results. In many cases, a trial and error procedure is used to select the optimal wavelet family. That is because there are no particular wavelet basis functions that can be applied to the entire speech signals. Therefore, it is necessary to analyze the similarity between the speech signal and the wavelet base function. One of the methods that can be used is cross-correlation. In this study, the degree of correlation is determined between wavelet base function and Indonesian vowels. The influence of gender and consistencies of the results are also used in the analysis. The results show that db45 and db44 are most similar to male and female vowels utterance, respectively. For consistencies, only vowel e gives a consistent result. Overall, db44 is most similar to all Indonesian vowels utterance.

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