Jakubek Stefan, Hametner Christoph
Department of Hybrid Powertrain Calibration and Battery Testing Technology, AVL-List GmbH, Graz, Austria.
IEEE Trans Syst Man Cybern B Cybern. 2009 Oct;39(5):1121-33. doi: 10.1109/TSMCB.2009.2013132. Epub 2009 Mar 24.
In this paper, nonlinear system identification utilizing generalized total least squares (GTLS) methodologies in neurofuzzy systems is addressed. The problem involved with the estimation of the local model parameters of neurofuzzy networks is the presence of noise in measured data. When some or all input channels are subject to noise, the GTLS algorithm yields consistent parameter estimates. In addition to the estimation of the parameters, the main challenge in the design of these local model networks is the determination of the region of validity for the local models. The method presented in this paper is based on an expectation-maximization algorithm that uses a residual from the GTLS parameter estimation for proper partitioning. The performance of the resulting nonlinear model with local parameters estimated by weighted GTLS is a product both of the parameter estimation itself and the associated residual used for the partitioning process. The applicability and benefits of the proposed algorithm are demonstrated by means of illustrative examples and an automotive application.
本文探讨了在神经模糊系统中利用广义总体最小二乘法（GTLS）进行非线性系统辨识的问题。神经模糊网络局部模型参数估计所涉及的问题是测量数据中存在噪声。当部分或所有输入通道受到噪声影响时，GTLS算法能产生一致的参数估计。除了参数估计外，这些局部模型网络设计中的主要挑战是确定局部模型的有效区域。本文提出的方法基于一种期望最大化算法，该算法利用GTLS参数估计的残差进行适当的划分。通过加权GTLS估计局部参数得到的非线性模型的性能，是参数估计本身以及用于划分过程的相关残差的产物。通过示例和汽车应用展示了所提算法的适用性和优势。
