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首页> 外文会议>International Conference on Information Science and Technology >A Quantized Kernel Least-mean-square Algorithm Based on Echo State Network for Online Time-series Prediction
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A Quantized Kernel Least-mean-square Algorithm Based on Echo State Network for Online Time-series Prediction

机译：基于回声状态网络的在线时间序列预测的量化内核最小均方算法

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摘要

Kernel least mean square (KLMS) algorithm is a popular method for online time-series prediction. It has the advantages of high robustness, low computational complexity, model simplicity, and online learning ability. Unfortunately, as input data grows, its dictionary size increases and the computational complexity rises significantly. In addition, how to improve the adaptability in uncertain environments with noise is also one of the main challenges. In this paper, we propose a Quantized Kernel Least-mean-square Based on Echo State Network (ES-QKLMS) algorithm in response to the above problems. In the new algorithm, the reservoirs of the echo state network (ESN) are integrated with the KLMS algorithm for online prediction. The ESN reservoir can store the associated information of historical data, which improves the algorithm’s ability to track temporal characteristics. Then, the vector quantization method is adopted to suppress the size of the KLMS dictionary. Moreover, the adaptive weight adjustment is used to improve the noise immunity in an uncertain environment. At last, The Lorenz time series and the El Nino-Southern Oscillation (ENSO) time series are discussed to demonstrate the efficacy of the ES-QKLMS algorithm.
机译：内核最小均方（KLMS）算法是用于在线时间序列预测的流行方法。它具有高稳健性，低计算复杂性，模型简单和在线学习能力的优点。不幸的是，随着输入数据的增长，其字典大小的增加，计算复杂性显着升高。此外，如何提高噪音不确定环境中的适应性也是主要挑战之一。在本文中，我们提出了一种基于回声状态网络（ES-QKLMS）算法的量化内核最小均线方形，响应于上述问题。在新算法中，回声状态网络（ESN）的储存器与用于在线预测的KLMS算法集成。 ESN水库可以存储历史数据的相关信息，从而提高了算法跟踪时间特征的能力。然后，采用矢量量化方法来抑制KLM字典的大小。此外，适应性重量调整用于改善不确定环境中的抗噪性。最后，讨论了Lorenz时间序列和EL Nino-Southern振荡（ENSO）时间序列以证明ES-QKLMS算法的功效。

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