达芬方程
噪音(视频)
声学
计算机科学
物理
控制理论(社会学)
人工智能
量子力学
非线性系统
图像(数学)
控制(管理)
标识
DOI:10.1098/rspa.2025.0002
摘要
Noise typically degrades the performance of physical systems. In some cases, however, noise can provide an enhanced effect. For instance, stochastic resonance may be observed in a nonlinear oscillator when a weak signal is boosted by noise. On the other hand, adaptive oscillators are a type of nonlinear oscillator that can learn and store information in dynamic states. Here, noise is shown to increase the learning rate of an adaptive oscillator, using a Duffing adaptive oscillator. To highlight this effect, stochastic simulations are employed, and the Fokker–Planck equation is semi-analytically solved using the cumulant neglect method. As adaptive oscillators can also be used as a powerful physical reservoir computer architecture, this work shows that the Duffing adaptive oscillator can be optimized for fast learning in noisy environments.
科研通智能强力驱动
Strongly Powered by AbleSci AI