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

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 di,erent 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 con0guration by assessing and comparing di,erent alternatives in cross-validation. In this paper, the two approaches are compared both as learning algorithms, and as identi0cation 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 identi0cation and control of a nonlinear system starting from simulated data are given. c © 2001 Elsevier Science B.V. All rights reserved.