The Mean Square Error (MSE) Performance Criteria
Adaptive signal processing algorithms generally attempt to optimize a performance measure that is a function of the unknown parameters to be identified. The most pervasive of these performance measures are based upon squared prediction errors, although the specific prediction error used in adaptation often depends upon the particular algorithm. Two broad categories of adaptive signal processing methods are: (1) stochastic and (2) exact. The latter category refers to adaptive filters based upon the actual or exact data signals acquired. The recursive least squares techniques comprising Chapters 8–11 are examples of these exact techniques, and investigation of those techniques will be deferred until the later chapters.
KeywordsMean Square Error Prediction Error Minimum Mean Square Error Linear Prediction Adaptive Filter
Unable to display preview. Download preview PDF.
- 3.J.T. Moore, Elementary Linear Algebra and Matrix Algebra: The Viewpoint of Geometry, McGraw-Hill, New York, 1972.Google Scholar
- 7.D.K. Fadeev and V.N. Fadeeva, Computational Methods of Linear Algebra, Free-man, San Francisco, 1963.Google Scholar
- 9.J.D. Markel and A.H. Gray, Linear Prediction of Speech, Springer-Verlag, New York, 1975.Google Scholar
- 11.L.R. Rabiner and R.W. Schafer, Digital Processing of Speech Signals, Prentice-Hall, Englewood Cliffs, NJ, 1978.Google Scholar
- 12.N.S. Jayant and P. Noll, Digital Coding of Waveforms, Prentice-Hall, Englewood Cliffs, NJ, 1984.Google Scholar
- 15.D.J. Wilde, Optimal Seeking Methods, Prentice-Hall, Englewood Cliffs, NJ, 1964.Google Scholar
- 16.M. Schwartz and L. Shaw, Signal Processing: Discrete Spectral Analysis, Detection, and Estimation, McGraw-Hill, New York, 1975.Google Scholar