Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.

IEEE transactions on neural networks Pub Date : 2011-09-01 Epub Date: 2011-07-29 DOI:10.1109/TNN.2011.2161330
Haiquan Zhao, Xiangping Zeng, Zhengyou He
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引用次数: 52

Abstract

To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.

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基于流水线双线性递归神经网络的低复杂度非线性自适应滤波器。
为了降低双线性递归神经网络(BLRNN)的计算复杂度,提出了一种基于流水线双线性递归神经网络(PBLRNN)的低复杂度非线性自适应滤波器。PBLRNN继承了Haykin和Li提出的流水线RNN的模块化架构,由多个以链式形式级联的BLRNN模块组成。每个模块由一个具有内部动态的小规模BLRNN实现。由于PBLRNN的这些模块可以以流水线并行的方式同时执行,这将导致计算效率的显著提高。此外,由于嵌套模块的存在,可以进一步提高PBLRNN的性能。为了适应模块化结构,在梯度下降法的基础上,提出了一种改进的自适应幅值实时循环学习算法。通过大量的仿真来评估PBLRNN在非线性系统识别、非线性信道均衡和混沌时间序列预测方面的性能。实验结果表明,与单BLRNN和RNN模型相比,PBLRNN具有明显更好的性能。
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来源期刊
IEEE transactions on neural networks
IEEE transactions on neural networks 工程技术-工程:电子与电气
自引率
0.00%
发文量
2
审稿时长
8.7 months
期刊最新文献
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