A number of researches, devoted to the computer based analysis of nonlinear effects in system identification and adaptive control of such systems, are continuously increasing over a time. Some problems were reviewed also in our previous monographies: Kazlauskas and Pupeikis 2016; Pupeikis 2017; Pupeikis and Kazlauskas 2021. The investigations were continued. Some actual problems have been explored. We begin here the monograph with the examination of the adaptive filters (Chap. 2), then, continuing about linear models, including the estimation of parameters of LPTV systems and AR processes (Chaps. 3 and 4), and examining the layered polynomial filter structures, used for nonlinear filters (Chap. 5). The next part of the book deals with the exploration of potentiality for the possibility of adaptive control of linear ARMAX systems with PWL nonlinearities (Chap. 6). The original approach, based on the linear block convolution, and used for reordering of the stored data for parametrical identification of nonlinear Hammerstein and Wiener systems having three or more segment PWL nonlinearities (Chaps. 7 and 8) is explored. It gives us a possibility to identify block-oriented systems with the invertible PWL nonlinearity as well as with the noninvertible one parametrically. It allows us for the noninvertible nonlinearity to build in the training phase the one-shot MV controller tuning, i.e. parametrical identification and controller parameter calculation routines are connected jointly only during the training time. Chapter 9 includes the extended computer-based experimental simulation and examination of MV/GMV control in a noisy frame.