Nonlinear Modeling of Dynamic Systems with the Self-Organizing Map

Abstract

In this paper we propose an unsupervised neural modeling technique, called Vector- Quantized Temporal Associative Memory (VQ-TAM). Using VQTAM, the Kohonen’s self-organizing map (SOM) becomes capable of approximating dynamical nonlinear mappings from time series of measured input-output data. The SOM produces modeling results as accurate as those produced by multilayer perceptron (MLP) networks, and better than those produced by radial basis functions (RBF) networks, both the MLP and the RBF based on supervised training. In addition, the SOM is less sensitive to weight initialization than MLP networks. The three networks are evaluated through simulations and compared with the linear ARX model in the forward modeling of a hydraulic actuator.