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Signal Optimal Smoothing by Means of Spectral Analysis
Advances in Statistical Methodologies and Their Application to Real Problems
This chapter introduces two new empirical methods for obtaining optimal smoothing of noise-ridden stationary and nonstationary, linear and nonlinear signals. Both methods utilize an application of the spectral representation theorem (SRT) for signal decomposition that exploits the dynamic properties of optimal control. The methods, named as SRT1 and SRT2, produce a low-resolution and a high-resolution ilter, which may be utilized for optimal long-and short-run tracking as well as forecastingdoi:10.5772/66150 fatcat:sfflmokzsza4pe7pruo4gdnvje