The local paradigm for modeling and control: from neuro-fuzzy to lazy learning

Abstract

The composition of simple local models for approximating complex nonlinear mappings is a common practice in recent modeling and control literature. This paper presents a comparative analysis of two different local approaches: the neuro-fuzzy inference system and the lazy learning approach. Neuro-fuzzy is a hybrid representation which combines the linguistic description typical of fuzzy inference systems, with learning procedures inspired by neural networks. Lazy learning is a memory-based technique that uses a query-based approach to select the best local model configuration by assessing and comparing different alternatives in cross-validation. In this paper, the two approaches are compared both as learning algorithms, and as identification modules of an adaptive control system. We show that lazy learning is able to provide better modeling accuracy and higher control performance at the cost of a reduced readability of the resulting approximator. Illustrative examples of identification and control of a nonlinear system starting from simulated data are given.