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首页> 外文期刊>WSEAS Transactions on Signal Processing >Identification of Third-order Volterra-PARAFAC models based on PARAFAC decomposition using a tensor approach
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Identification of Third-order Volterra-PARAFAC models based on PARAFAC decomposition using a tensor approach

机译：使用张量方法识别基于PARAFAC分解的三阶Volterra-Paraf模型

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Volterra models are very useful for representing nonlinear systems with vanishing memory. The main drawback of these models is their huge number of parameters to be estimated. In this paper, we present a new class of Volterra models, called Volterra-Parafac models, with a reduced parametric complexity, by considering Volterra kernels of order (p > 2) as symmetric tensors and by using a parallel factor (PARAFAC) decomposition. This paper is concerned with the problem of identification of third-order Volterra-PARAFAC models. Two types of algorithms are proposed for estimating the parameters of these models when input-output signals and kernel coefficients are real valued. The first is called Levenberg-Marquardt algorithm and the second is the Partial Update LMS algorithms. Some simulation results illustrate the proposed identification methods.
机译：Volterra模型对于代表具有消失存储器的非线性系统非常有用。 这些模型的主要缺点是它们估计大量参数。 在本文中，我们展示了一类新的Volterra模型，称为Volterra-Parafac型号，通过考虑阶数（P> 2）作为对称张量的Volterra内核并使用平行因子（PARAFAC）分解来降低参数复杂性。 本文涉及第三阶Volterra-Paraf模型的识别问题。 当输入输出信号和核系数是真值时，提出了两种类型的算法用于估计这些模型的参数。 第一个称为Levenberg-Marquardt算法，第二个是部分更新LMS算法。 一些仿真结果说明了所提出的识别方法。

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