模糊大脑情感学习的自适应噪声抵消方法

Adaptive Noise Cancellation for Fuzzy Brain Emotional Learning

提出一种以模糊大脑情感学习(fuzzy brain emotional learning,FBEL)模型作为自适应噪声抵消器的噪声抵消方法,应用于信号处理问题。该方法通过FBEL模拟经非线性通道传输后的噪声信号,将噪声信号从含噪信号中过滤掉,达到消噪的目的;根据自适应学习算法,利用奖励信号和梯度下降法对FBEL模型的权重及参数进行在线更新,以适应噪声的变化。选取均方根误差和计算时间2个性能指标,采用自适应噪声抵消方法在不同网络中进行仿真比较,结果表明,应用该方法可以获得更好的滤波性能。

A noise cancellation method is proposed using fuzzy brain emotional learning(FBEL)model as an adaptive noise canceller to simulate the noise signal transmitted through the nonlinear channel,and filters out the noise signal from the signal containing noise to achieve the purpose of noise cancellation.The weights and parameters of the fuzzy brain emotional learning model are also updated on the use of the reward signal and gradient decline in the adaptive learning algorithm to adapt to changes in noise.Two performance indicators of the root mean square error and the computation time are then selected and simulation results of adaptive noise cancellation method in different networks compared,which shows that the application of adaptive noise canceller can achieve better filtering performance.