Identification and Control of Dynamical Systems Using The Self-Organizing Map  

Oleh:

Araujo, A. F. R. ; Barreto, G. A.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 5 (Sep. 2004), page 1244-1259.
Topik: control; identification; control; dynamical systems; self - organizing

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.11 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper, we introduce a general modeling technique, called vector - quantized temporal associative memory (VQTAM), which uses Kohonen's self - organizing map (SOM) as an alternative to multilayer perceptron (MLP) and radial basis function (RBF) neural models for dynamical system identification and control. We demonstrate that the estimation errors decrease as the SOM training proceeds, allowing the VQTAM scheme to be understood as a self-supervised gradient - based error reduction method. The performance of the proposed approach is evaluated on a variety of complex tasks, namely : i) time series prediction ; ii) identification of SISO/MIMO systems ; and iii) non linear predictive control. For all tasks, the simulation results produced by the SOM are as accurate as those produced by the MLP network, and better than those produced by the RBF network. The SOM has also shown to be less sensitive to weight initialization than MLP networks. We conclude the paper by discussing the main properties of the VQTAM and their relationships to other well established methods for dynamical system identification. We also suggest directions for further work.

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