基于管道递归模糊网络的非线性自适应语音预测

D. Stavrakoudis, J. Theocharis
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引用次数: 2

摘要

本文提出了一种用于非线性自适应信号预测的流水线tsk型递归模糊网络(PTRFN)。PTRFN模型由许多以级联形式相互连接的模块组成。参与模块通过带有内部动态的递归模糊神经网络实现。模块的结构从输入-输出数据依次演化。采用梯度下降算法和扩展最小二乘法完成参数学习任务。所建议的预测器显示了一系列吸引人的属性,包括有效的时间模式的空间表示、增强的记忆能力和较低的计算复杂性。对自适应语音预测问题进行了非线性预测器(PTRFN)和线性预测器(抽头延延线滤波器)的测试。仿真结果表明,与已有的递归网络相比,该网络具有较好的性能
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonlinear Adaptive Speech Prediction using a Pipelined Recurrent Fuzzy Network
In this paper, a pipelined TSK-type recurrent fuzzy network (PTRFN) is proposed for nonlinear adaptive signal prediction. The PTRFN model consists of a number of modules interconnected in a cascaded form. The participating modules are implemented through recurrent fuzzy neural networks with internal dynamics. The structure of the modules is evolved sequentially from input-output data. The parameter learning task is accomplished using a gradient descent algorithm and the extended least squares method. The suggested predictor exhibits a series of attractive attributes, including effective spatial representation of the temporal patterns, enhanced memorizing capabilities, and low computational complexity. The nonlinear subsection of the predictor (PTRFN), followed by a linear subsection (a tapped delay-line filter) is tested on the adaptive speech prediction problem. Simulation results demonstrate that considerably better performance is obtained compared with other existing recurrent networks
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