| Cognitive Models for Learning to Control Dynamic Systems |
26-Sep-2008 |
33 pages |
| Authors:
Zhong-Ping Jiang; APPLIED SR TECHNOLOGIES INC BROOKLYN NY
|
 | Wald's sequential probability ratio test (SPRT) model and the equivalent discrete drift diffusion model have been widely used to explain human and animal decision making in psychophysical tasks. These models assume that observers gradually accumulate evidence from noisy inputs and make a decision when the evidence reaches a threshold. It is discovered that stochastic-resonance (SR) like behavior arises in the SPRT model when the actual input signal is significantly weaker ... |
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| Nonlinear Bistable Detectors and Arrival-Time Estimators Based on Parameter-Tuning Stochastic Resonance |
DEC 2006 |
12 pages |
| Authors:
Xingxing Wu; Zhong-Ping Jiang; Bohou Xu; Daniel W. Repperger; POLYTECHNIC INST OF NEW YORK BROOKLYN DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
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| Parameter-tuning stochastic resonance (PSR) technique provides a new approach for signal processing. This paper will first fill the gap in the performance analysis of the nonlinear PSR-based detector by comparing it with the matched filter detector under both ideal conditions (white Gaussian noise, and perfect, synchronization) and non-ideal conditions (colored noise, de-synchronization, and low sampling rate) to identify its strengths and weaknesses. |
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| Enhancement of Stochastic Resonance Using Optimization Theory |
SEP 2006 |
22 pages |
| Authors:
Xingxing Wu; Zhong-Ping Jiang; Daniel W. Repperger; Yi Guo; POLYTECHNIC UNIV BROOKLYN NY
|
| The traditional stochastic resonance is realized by adding an optimal amount of noise, while the parameter tuning stochastic resonance is realized by optimally tuning the system parameters. The further improvement of the maximal normalized power norm of the bistable double-well dynamic system with white Gaussian noise input can be converted to an optimization problem with constraints on system parameters and noise intensity, which is proven to have one and only ... |
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| Theoretical Analysis of Image Processing Using Parameter-Tuning Stochastic Resonance Technique |
SEP 2006 |
8 pages |
| Authors:
Bohou Xu; Xingxing Wu; Zhong-Ping Jiang; Daniel W. Repperger; ZHEJIANG UNIV HANGZHOU (CHINA) DEPT OF MECHANICS
|
| Parameter-tuning stochastic resonance (PSR) technique provides a new approach for signal processing. This paper will first fill the gap in the performance analysis of the nonlinear PSR-based detector by comparing it with the matched filter detector by comparing it with the matched filter detector under both ideal conditions (white Gaussian noise, and perfect synchronization) and no-ideal conditions (colored noise, desynchronization, and low sampling rate) to identify its strengths and weaknesses. ... |
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| Enhancement of Stochastic Resonance by Tuning System Parameters and Adding Noise Simultaneously |
NOV 2005 |
8 pages |
| Authors:
Xingxing Wu; Zhong-Ping Jiang; Daniel Repperger; POLYTECHNIC UNIV BROOKLYN NY DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
|
| The stochastic resonance effect can be realized by tuning system parameters or by adding noise. This paper investigates the possibility to enhance the stochastic resonance effect by tuning system parameters and adding noise simultaneously. First, we use some examples to demonstrate the situation where only the system parameters or noise can be adjusted for maximizing the stochastic resonance effect. Then, it is shown using standard optimization theory that the normalized ... |
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| Enhancement of Stochastic Resonance by Tuning System Parameters and Adding Noise Simultaneously |
NOV 2005 |
8 pages |
| Authors:
Xingxing Wu; Zhong-Ping Jiang; Daniel Repperger; POLYTECHNIC UNIV BROOKLYN NY
|
| The stochastic resonance effect can be realized by tuning system parameters or by adding noise. This paper investigates the possibility to enhance the stochastic resonance effect by tuning system parameters and adding noise simultaneously. First, we use some examples to demonstrate the situation where only the system parameters or noise can be adjusted for maximizing the stochastic resonance effect. Then, it is shown using standard optimization theory that the normalized ... |
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