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摘要: 针对一步预测问题,本文提出一种新的自适应滤波算法,该算法通过神经网络来调制薛定谔方程的势场函数.这种算法就是所谓的量子递归神经网络(RQNN),它可以过滤嵌入在真实信号中的非平稳噪声且不需要信号和噪声的任何先验信息.本文通过RQNN与RLS算法的仿真结果比较,表明:RQNN在过滤嵌入在直流信号,正弦信号,阶梯信号和语言信号中的高斯平稳噪声,高斯非平稳噪声或非高斯平稳噪声更准确和有更好的自适应性.实验结果表明:RQNN在过滤正弦信号中的高斯噪声时,输出信噪比相对于输入信噪比提高了20dB,这比RLS滤波器高10dB.
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关键词:
- 自适应滤波 /
- 量子力学 /
- 量子递归神经网络(RQNN)
Abstract: This paper provides a new adaptive algorithm for single-step prediction by modeling the potential field of a one dimension Schrdinger wave equation using neural network. This new architecture is referred to as the recurrent quantum neural network (RQNN). The RQNN can filter the signal embedded with non-stationary noise without any priori knowledge of the shape of the signal and statistical properties of the noise. We compared the simulation results of the RQNN with a classical adaptive stochastic filter-RLS. It is shown that the RQNN is much more efficient in denoising signals embedded with Gaussian stationary, non-Gaussian stationary and Gaussian nonstationary noise such as DC, sinusoid, staircase and speech signals. The RQNN can enhance the signal to noise rate (SNR) by 20 dB, which is more than 10 dB given by the traditional technology when it denoising sinusoid signal. -
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