Fractional stochastic resonance multi-parameter adaptive optimization algorithm based on genetic algorithm
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SMART DATA AGGREGATION INSPIRED PARADIGM & APPROACHES IN IOT APPLNS
Fractional stochastic resonance multi-parameter adaptive optimization algorithm based on genetic algorithm Yongjun Zheng1
•
Ming Huang1 • Yi Lu1 • Wenjun Li1
Received: 8 October 2018 / Accepted: 17 November 2018 Springer-Verlag London Ltd., part of Springer Nature 2018
Abstract The output effect of fractional-order stochastic resonance (FOSR) system is affected by many factors such as input system parameters and noise intensity. In practice, many tests are needed to adjust parameters to achieve the optimal effect, and this way of ‘‘trial and error’’ greatly limits the application prospect of FOSR. Based on genetic algorithm, a suitable adaptive function was established to adjust the multiple parameters, including the fractional order, system parameters, and the input noise intensity of the fractional bistable system. Simulation results showed that the algorithm can achieve joint optimization of these parameters. It was proved that this algorithm is conducive to the real-time adaptive adjustment of the FOSR system in practical applications and conducive to the application and extension of FOSR in weak signal detection and other fields. The proposed algorithm has certain practical value. Keywords Real-time adaptive adjustment Bistable system Simulation Weak signal detection
1 Introduction Stochastic resonance (SR) refers to the fact that in signal processing, it is possible to increase the signal-to-noise ratio (SNR) at the output of a nonlinear system by adding a certain amount of noise to the input [1]. It also provides a new way to deal with the weak signal’s noise processing which is different from traditional ones. Previous studies on SR were mainly about integer-order systems, but when dealing with non-Markovian complex systems with memory effects on the past states, conventional integer-order SR has great limitations [2–4]. However, fractional-order SR (FOSR), due to its temporal memory characteristics, has more advantages in describing and studying complex systems. At present, studies on FOSR have been improved very much, and its applications have also involved many disciplines, including biomedicine. For example, studying the
& Yongjun Zheng [email protected] 1
College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, Zhejiang, China
intramolecular diffusion of single protein, using a fractional-order model to describe the changes in the composition and structure of cell membrane [5], mechanical fault detection such as using adaptive SR model based on artificial fish swarm algorithm for mechanical bearing fault signal analysis and processing [6] and using fractional differential operators to describe anomalous diffusion in the Bloch Torrey equation in the dynamics domain [7]. However, the experimental simulation and practical application of FOSR still mainly use experiential trial-anderror methods to adjust system parameters, where the redundant oper
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