Novel approaches for parameter estimation of local linear models for dynamical system identification

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 44; no. 1; pp. 149 - 165
• Main Authors: Barreto, Guilherme A., M. Souza, Luís Gustavo
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2016; Springer Nature B.V
• Subjects: Algorithms; Approximation; Artificial Intelligence; Computer Science; Construction; Dynamical systems; Identification; Machines; Manufacturing; Mathematical analysis; Mechanical Engineering; Neural networks; Neurons; Parameter estimation; Partitions; Performance evaluation; Processes; Vectors (mathematics); Global models; Feedforward neural networks; Vector quantization; Self-organizing maps; Local linear models; Nonlinear system identification

In this paper we introduce two novel techniques for local linear modeling of dynamical systems. As in the standard approach, we use vector quantization (VQ) algorithms, such as the Self-Organizing Map, to partition the joint input-output space into smaller regions. Then, to each neuron we associate...