scholarly journals Realising the decomposition of a multi‐frequency signal under the coloured noise background by the adaptive stochastic resonance in the non‐linear system with periodic potential

2018 ◽  
Vol 12 (7) ◽  
pp. 930-936 ◽  
Author(s):  
Xiaogang Huang ◽  
Jingling Zhang ◽  
Meilei Lv ◽  
Gang Shen ◽  
Jianhua Yang
Keyword(s):  
Linear System ◽  
Stochastic Resonance ◽  
Periodic Potential ◽  
Frequency Signal ◽  
Coloured Noise ◽  
Noise Background ◽  
Non Linear ◽  
Non Linear System

scholarly journals How Does the Input Signal Shape Affect the Pass Efficiency After Stochastic Resonance?

2021 ◽  
Author(s):  
Richard Xu
Keyword(s):  
Linear System ◽  
Stochastic Resonance ◽  
Input Signal ◽  
Signal To Noise Ratio ◽  
Schmitt Trigger ◽  
Signal Wave ◽  
System A ◽  
Non Linear ◽  
Output Efficiency ◽  
Non Linear System

Stochastic Resonance is a phenomenon first discovered in 1981. The phenomenon describes that under certain conditions, in a non-linear system, a noise added to an input can make the input signal pass the non-linear barrier. This study will investigate how the shape of the input signal wave can affect the output efficiency. A circuit with a noise source, an AC source, a Schmitt trigger (act as the non-linear system) is simulated. Various shapes of the wave were tested in the simulator, which resulted in different spectrums and voltage-time graphs of the output and output efficiencies. After comparing the results for different wave shapes, the pulse wave and the square wave are observed to have the highest output efficiency and signal-to-noise ratio, followed by sinusoidal wave, triangular wave, and sawtooth wave in that order.

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.

On a non-linear system for shape memory alloys

1990 ◽  
Vol 2 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Ph. B�nilan ◽  
D. Blanchard ◽  
H. Ghidouche
Keyword(s):  
Linear System ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Non Linear ◽  
Non Linear System
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