On building local models for inverse system identification with vector quantization algorithms

In this paper we provide a comprehensive performance evaluation of vector quantization (VQ) algorithms as building blocks for designing local models for inverse system identification. We describe how VQ algorithms can be used for learning compact representations of the task of interest from available input–output time series data and how this representation can be used to build local maps that approximates the global inverse model of the system. The performances of the resulting local models are compared to the standard global (multilayer perceptron) MLP-based model in the task of inverse modeling of four well-known single input–single output (SISO) systems. The obtained results show that VQ-based local models perform better than the MLP in all the studied tasks.