scholarly journals Wiener-Hammerstein model and its learning for nonlinear digital pre-distortion of optical transmitters

2020 ◽  
Vol 28 (21) ◽  
pp. 30952
Author(s):  
Takeo Sasai ◽  
Masanori Nakamura ◽  
Etsushi Yamazaki ◽  
Asuka Matsushita ◽  
Seiji Okamoto ◽  
...  
Keyword(s):  
Hammerstein Model ◽  
Optical Transmitters

A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Akshaykumar Naregalkar ◽  
Subbulekshmi Durairaj
Keyword(s):  
Linear System ◽  
Regulatory Control ◽  
Hammerstein Model ◽  
Support Vector ◽  
Model Structure ◽  
Linear Dynamics ◽  
Vector Machines ◽  
Non Linear ◽  
Laguerre Filters ◽  
Non Linear System

Abstract A continuous stirred tank reactor (CSTR) servo and the regulatory control problem are challenging because of their highly non-linear nature, frequent changes in operating points, and frequent disturbances. System identification is one of the important steps in the CSTR model-based control design. In earlier work, a non-linear system model comprises a linear subsystem followed by static nonlinearities and represented with Laguerre filters followed by the LSSVM (least squares support vector machines). This model structure solves linear dynamics first and then associated nonlinearities. Unlike earlier works, the proposed LSSVM-L (least squares support vector machines and Laguerre filters) Hammerstein model structure solves the nonlinearities associated with the non-linear system first and then linear dynamics. Thus, the proposed Hammerstein’s model structure deals with the nonlinearities before affecting the entire system, decreasing the model complexity and providing a simple model structure. This new Hammerstein model is stable, precise, and simple to implement and provides the CSTR model with a good model fit%. Simulation studies illustrate the benefit and effectiveness of the proposed LSSVM-L Hammerstein model and its efficacy as a non-linear model predictive controller for the servo and regulatory control problem.

scholarly journals Early Detection of Defects through the Identification of Distortion Characteristics in Ultrasonic Responses

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 850
Author(s):  
Pietro Burrascano ◽  
Matteo Ciuffetti
Keyword(s):  
Early Detection ◽  
Additive Noise ◽  
Linear Models ◽  
High Stress ◽  
Damage Index ◽  
Hammerstein Model ◽  
Linear Behavior ◽  
Non Linear ◽  
Ultrasonic Techniques ◽  
Detection Of Defects

Ultrasonic techniques are widely used for the detection of defects in solid structures. They are mainly based on estimating the impulse response of the system and most often refer to linear models. High-stress conditions of the structures may reveal non-linear aspects of their behavior caused by even small defects due to ageing or previous severe loading: consequently, models suitable to identify the existence of a non-linear input-output characteristic of the system allow to improve the sensitivity of the detection procedure, making it possible to observe the onset of fatigue-induced cracks and/or defects by highlighting the early stages of their formation. This paper starts from an analysis of the characteristics of a damage index that has proved effective for the early detection of defects based on their non-linear behavior: it is based on the Hammerstein model of the non-linear physical system. The availability of this mathematical model makes it possible to derive from it a number of different global parameters, all of which are suitable for highlighting the onset of defects in the structure under examination, but whose characteristics can be very different from each other. In this work, an original damage index based on the same Hammerstein model is proposed. We report the results of several experiments showing that our proposed damage index has a much higher sensitivity even for small defects. Moreover, extensive tests conducted in the presence of different levels of additive noise show that the new proposed estimator adds to this sensitivity feature a better estimation stability in the presence of additive noise.

The identification of neuro-fuzzy based MIMO Hammerstein model with separable input signals

2016 ◽  
Vol 174 ◽  
pp. 530-541 ◽  
Author(s):  
Li Jia ◽  
Xunlong Li ◽  
Min-Sen Chiu
Keyword(s):  
Hammerstein Model ◽  
Neuro Fuzzy ◽  
Input Signals

scholarly journals Design of Self-Tuning Regulator for Large-Scale Interconnected Hammerstein Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Mourad Elloumi ◽  
Samira Kamoun
Keyword(s):  
Large Scale ◽  
Recursive Algorithm ◽  
Minimum Variance ◽  
Parametric Estimation ◽  
Hammerstein Model ◽  
Stochastic Nonlinear Systems ◽  
Hydraulic Systems ◽  
Self Tuning ◽  
Variance Approach ◽  
Error Method

This paper deals with the self-tuning regulator for large-scale stochastic nonlinear systems, which are composed of several interconnected nonlinear monovariable subsystems. Each interconnected subsystem is described by discrete Hammerstein model with unknown and time-varying parameters. This self-tuning control is developed on the basis of the minimum variance approach and is combined by a recursive algorithm in the estimation step. The parametric estimation step is performed on the basis of the prediction error method and the least-squares techniques. Simulation results of the proposed self-tuning regulator for two interconnected nonlinear hydraulic systems show the reliability and effectiveness of the developed method.

Thermoelectric cooler identification based on continuous-time Hammerstein model using metaheuristics algorithm

2021 ◽  
Author(s):  
Julakha Jahan Jui ◽  
Mohd Ashraf Ahmad ◽  
Mohamed Sultan Mohamed Ali ◽  
Mohd Anwar Zawawi ◽  
Mohd Falfazli Mat Jusof
Keyword(s):  
Continuous Time ◽  
Hammerstein Model ◽  
Thermoelectric Cooler
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