A discrete-time learning control algorithm for a class of linear time-invariant systems

1995 ◽  
Vol 40 (6) ◽  
pp. 1138-1142 ◽  
Author(s):  
S.S. Saab
2021 ◽  
pp. 562-598
Author(s):  
Stevan Berber

Due to the importance of the concept of independent variable modification, the definition of linear-time-invariant system, and their implications for discrete-time signal processing, Chapter 11 presents basic deterministic continuous-time signals and systems. These signals, expressed in the form of functions and functionals such as the Dirac delta function, are used throughout the book for deterministic and stochastic signal analysis, in both the continuous-time and the discrete-time domains. The definition of the autocorrelation function, and an explanation of the convolution procedure in linear-time-invariant systems, are presented in detail, due to their importance in communication systems analysis and synthesis. A linear modification of the independent continuous variable is presented for specific cases, like time shift, time reversal, and time and amplitude scaling.


2019 ◽  
Vol 64 (11) ◽  
pp. 4717-4724 ◽  
Author(s):  
Wentao Tang ◽  
Zhenhua Wang ◽  
Ye Wang ◽  
Tarek Raissi ◽  
Yi Shen

2021 ◽  
pp. 690-713
Author(s):  
Stevan Berber

Due to the importance of the concept of independent discrete variable modification and the definition of discrete linear-time-invariant systems, Chapter 14 presents and discusses basic deterministic discrete-time signals and systems. These discrete signals, which are expressed in the form of functions, including the Kronecker delta function and the discrete rectangular pulse, are used throughout the book for deterministic discrete signal analysis. The chapter also presents the definition of the autocorrelation function and the explanation of the convolution procedure in linear-time-invariant systems for discrete-time signals in detail, due to the importance of these in the analysis and synthesis of discrete communication systems.


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