Publications (19) View all
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Article: Nonlinear system identification by Gustafson-Kessel fuzzy clustering and supervised local model network learning for the drug absorption spectra process.
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ABSTRACT: This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.e., unsupervised learning, is applied. In combination with the LMN learning procedure, a new incremental method to define the number and the initial locations of the cluster centers for the GK clustering algorithm is proposed. Each data cluster corresponds to a local region of the process and is modeled with a local linear model. Since the validity functions are calculated from the fuzzy covariance matrices of the clusters, they are highly adaptable and thus the process can be described with a very sparse amount of local models, i.e., with a parsimonious LMN model. The proposed method for constructing the LMN is finally tested on a drug absorption spectral process and compared to two other methods, namely, Lolimot and Hilomot. The comparison between the experimental results when using each method shows the usefulness of the proposed identification algorithm.IEEE Transactions on Neural Networks 12/2011; 22(12):1941-51. · 2.95 Impact Factor -
Conference Proceeding: Model-based design of experiments based on local model networks for nonlinear processes with low noise levels
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ABSTRACT: Most common methods for experiment design are classical, geometric designs and optimal designs. Both categories of methods don't incorporate specific information about the process behavior into the design of experiments. In the case of optimal design often the underlying model structure is chosen as low order polynomial which is very restricted in its flexibility and causes problems, if used for higher-dimensional problems. Furthermore, the focus of these approaches lies on the minimization of the variance error. However, in many applications the process noise is negligible in comparison to the highly nonlinear behavior which usually causes a large bias error. Therefore, this paper presents the new algorithm HilomotDoE which is an active learning algorithm that aims to minimize the bias error of the model. This is achieved by an iterative refinement of a local model network and simultaneously the addition of a certain amount of measurement points. Demonstration examples and theoretical comparisons with the common D-optimal design show the usefulness of HilomotDoE for the mentioned problem class.American Control Conference (ACC), 2011; 08/2011 -
Conference Proceeding: Hierarchical local model trees for design of experiments in the framework of ultrasonic structural health monitoring.
Proceedings of the IEEE International Conference on Control Applications, CCA 2011, Denver, CO, USA, September 28-30, 2011; 01/2011 -
Article: Supervised Hierarchical Clustering in Fuzzy Model Identification.
IEEE T. Fuzzy Systems. 01/2011; 19:1163-1176. -
Article: Nonlinear System Identification by Gustafson-Kessel Fuzzy Clustering and Supervised Local Model Network Learning for the Drug Absorption Spectra Process.
IEEE Transactions on Neural Networks. 01/2011; 22:1941-1951.
