一种基于RBF网络自适应算法的定位算法

Jin Ren, Jingxing Chen, Liang Feng
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引用次数: 2

摘要

摘要泰勒级数展开(Taylor series展开,TSE)方法由于具有良好的鲁棒性和定位精度,在求解非线性方程中得到了广泛的应用,近年来受到了广泛的关注。针对基于RBF神经网络的泰勒级数展开定位算法的性能高度依赖于初始估计的问题,提出了一种基于RBF神经网络的泰勒级数展开定位算法(RBF-TSE)。为了提高定位精度和降低定位成本,提出了一种基于自适应RBF神经网络的泰勒级数扩展定位算法(SA-RBF-TSE)。本文对该算法进行了分析,并与其他几种算法进行了仿真。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Positioning Algorithm Based on Self-adaptive Algorithm of RBF Network
Abstract: Much attention has been paid to Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning. A Taylor-series expansion location algorithm based on the RBF neural network (RBF-TSE) is proposed before to the performance of TSE highly depends on the initial estimation. In order to have more accurate and lower cost,a new Taylor-series expansion location algorithm based on Self-adaptive RBF neural network (SA-RBF-TSE) is proposed to estimate the initial value. The proposed algorithm is analysed and simulated with several other algorithms in this paper.
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