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An Adaptive Stochastic Resonance System and Method Based on Artificial Fish Swarm Algorithm

An artificial fish swarm algorithm and stochastic resonance technology, applied in computing, computing models, instruments, etc., can solve the problems of weak improvement of the signal-to-noise ratio of the output of the particle swarm algorithm, huge impact on the global search ability, and slow convergence speed, etc., to improve the system Signal-to-noise ratio gain, reduced system complexity, and improved adaptability

Active Publication Date: 2019-10-11
THE PLA INFORMATION ENG UNIV
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Problems solved by technology

[0003] The present invention aims at the shortcomings of the existing ant colony algorithm, such as long search time, slow convergence speed, and great influence of parameter selection on the global search ability, and the problem that the particle swarm algorithm has weak output signal-to-noise ratio improvement, etc., and proposes an artificial fish swarm algorithm Adaptive Stochastic Resonance System and Method of

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  • An Adaptive Stochastic Resonance System and Method Based on Artificial Fish Swarm Algorithm
  • An Adaptive Stochastic Resonance System and Method Based on Artificial Fish Swarm Algorithm
  • An Adaptive Stochastic Resonance System and Method Based on Artificial Fish Swarm Algorithm

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Embodiment 1

[0043] Example 1: Combining Figure 1-Figure 5 , an adaptive stochastic resonance system based on the artificial fish swarm algorithm, including a normalized stochastic resonance module, an artificial fish swarm adaptive module, a step size adjustment module, a noise adjustment module and an iterative control module, wherein,

[0044] Normalized stochastic resonance module: combine the original signal with stochastic resonance normalization processing, and send it to the artificial fish adaptive module;

[0045] Artificial fish swarm module: According to the feedback results of the step size adjustment module and the noise adjustment module, iterate the stochastic resonance parameters for fish swarm reproduction, and feed back the results to the iterative control module;

[0046] The step adjustment module: adjust the fish swarm iteration step size control convergence speed according to the adaptive parameter optimization convergence state, and feed back the step size adjustme...

Embodiment 2

[0075] Example 2: Combining Figure 1-Figure 5 , an adaptive stochastic resonance system based on the artificial fish swarm algorithm, including a normalized stochastic resonance module 101 , an artificial fish swarm module 102 , a step adjustment module 103 , a noise adjustment module 104 and an iterative control module 105 . Among them, the normalized stochastic resonance module 101 combines the original periodic signal with stochastic resonance normalization processing, and sends it to the artificial fish shoal module 102; the artificial fish shoal module 102 adjusts the random Resonance output results are iterated for fish school reproduction, and the results are fed back to the iterative control module 105 for iterative control; the step size adjustment module 103 adjusts the fish school iteration step size control convergence speed according to the adaptive parameter optimization convergence status, and feeds back the step size adjustment result To the artificial fish sc...

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Abstract

The invention belongs to the communication signal detection field and the non-linear field and particularly relates to an adaptive stochastic resonance system and method based on an artificial fish swarm algorithm. The system comprises a normalized stochastic resonance module, an artificial fish swarm adaptive module, a step adjustment module, a noise adjustment module and an iteration control module. Through the system, not only can a problem of difficulty in setting an optimization searching initial value of an adaptive system be solved through normalized stochastic resonance, adaptability is improved, no stochastic resonance, excessive stochastic resonance and insufficient resonance caused by parameter selection are considered, relatively strong search capability is kept through a step function adjusted according to resonance convergence conditions, but also local optimum can be properly discovered and jumped out from, and optimal value convergence efficiency is accelerated. An input signal to noise ratio is optimized through the noise adjustment module, and system signal to noise ratio gain is substantially improved.

Description

technical field [0001] The invention belongs to the field of communication signal detection, in particular to an adaptive stochastic resonance system and method based on an artificial fish swarm algorithm. Background technique [0002] Weak signal detection technology under noise background has been widely researched and developed in communication, machinery, medicine, physical measurement and other fields. Compared with traditional signal detection methods, stochastic resonance can effectively use noise energy to enhance periodic signals while suppressing noise energy, which will bring great help to low signal-to-noise ratio signal detection technology. Therefore, the concept of stochastic resonance has been paid attention to in the field of signal processing. However, the occurrence of stochastic resonance is not unconditional. It needs the signal, noise, and nonlinear system to meet certain matching conditions to enhance the stochastic resonance effect and improve the si...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/00
Inventor 孔德阳马金全彭华李妍巩克现李天云杜建王彬吴微李丹丹
Owner THE PLA INFORMATION ENG UNIV
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