scholarly journals Dynamic response and stochastic resonance of a tri-stable system

2015 ◽  
Vol 64 (20) ◽  
pp. 200503 ◽  
Author(s):  
Lai Zhi-Hui ◽  
Leng Yong-Gang
Keyword(s):  
Dynamic Response ◽  
Stochastic Resonance ◽  
Stable System

Virtual Instrument for Weak Signal Detecting on Monostable Stochastic Resonance

2011 ◽  
Vol 279 ◽  
pp. 361-366
Author(s):  
Quan Yuan ◽  
Yan Shen ◽  
Liang Chen
Keyword(s):  
Power Spectrum ◽  
White Noise ◽  
Stochastic Resonance ◽  
Virtual Instrument ◽  
Stable System ◽  
Periodic Signal ◽  
Nonlinear Phenomenon ◽  
Weak Signal ◽  
Additive White Noise ◽  
Periodic Signals

Stochastic resonance (SR) is a nonlinear phenomenon which can be used to detect weak signal. The theory of SR in a biased mono-stable system driven by multiplicative and additive white noise as well as a weak periodic signal is investigated. The virtual instrument (VI) for weak signal detecting based on this theory is designed with LabVIEW. This instrument can be used to detect weak periodic signals which meets the conditions given and can greatly improved the power spectrum of the weak signal. The results that related to different sets of parameters are given and the features of these results are in accordance with the theory of mono-stable SR. Thus, the application of this theory in the detecting of weak signal is proven to be valid.

Stochastic Resonance in Nonequilibrium Single-Stable System

1992 ◽  
Vol 17 (4) ◽  
pp. 495-498
Author(s):  
Jing-Dong Bao ◽  
Yi-Zhong Zhuo ◽  
Xi-Zhen Wu
Keyword(s):  
Stochastic Resonance ◽  
Stable System

Stochastic resonance in multi-stable system driven by Lévy noise

2018 ◽  
Vol 56 (4) ◽  
pp. 1559-1569 ◽  
Author(s):  
Dongying Han ◽  
Xiao Su ◽  
Peiming Shi
Keyword(s):  
Stochastic Resonance ◽  
Stable System ◽  
Lévy Noise ◽  
Levy Noise

scholarly journals Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6719
Author(s):  
Omar Rodríguez-Abreo ◽  
Francisco Antonio Castillo Velásquez ◽  
Jonny Paul Zavala de Paz ◽  
José Luis Martínez Godoy ◽  
Crescencio Garcia Guendulain
Keyword(s):  
Neural Networks ◽  
Transfer Function ◽  
Dynamic Response ◽  
High Performance ◽  
Response Prediction ◽  
Stable System ◽  
Network Systems ◽  
Multiple Systems ◽  
Data Points ◽  
Basic Characteristics

In the present work, a neuronal dynamic response prediction system is shown to estimate the response of multiple systems remotely without sensors. For this, a set of Neural Networks and the response to the step of a stable system is used. Six basic characteristics of the dynamic response were extracted and used to calculate a Transfer Function equivalent to the dynamic model. A database with 1,500,000 data points was created to train the network system with the basic characteristics of the dynamic response and the Transfer Function that causes it. The contribution of this work lies in the use of Neural Network systems to estimate the behavior of any stable system, which has multiple advantages compared to typical linear regression techniques since, although the training process is offline, the estimation can perform in real time. The results show an average 2% MSE error for the set of networks. In addition, the system was tested with physical systems to observe the performance with practical examples, achieving a precise estimation of the output with an error of less than 1% for simulated systems and high performance in real signals with the typical noise associated due to the acquisition system.

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