Paper Info
Title
Local Linear Model Trees for On-Line Identification of Time-Variant Nonlinear Dynamic Systems
Abstract
This paper discusses on-line identification of time-variant nonlinear dynamic systems. A neural network (LOLIMOT, [1]) based on local linear models weighted by basis functions and constructed by a tree algorithm is introduced. Training of this network can be divided into a structure and a parameter optimization part. Since the network is linear in its parameters a recursive least-squares algorithm can be applied for on-line identification. Other advantages of the proposed local approach are robustness and high training and generalisation speed. The simplest recursive version of the algorithm requires only slightly more computations than a recursive linear model identification. The locality of LOLIMOT enables on-line learning in one operating region without forgetting in the others. A drawback of this approach is that systems with large structural changes over time cannot be properly identified, since the model structure is fixed.
Year
DOI
Venue
1996
10.1007/3-540-61510-5_23
ICANN
Keywords
Field
DocType
time-variant nonlinear dynamic systems,local linear model trees,on-line identification,neural network,structural change,linear model
Locality,Mathematical optimization,Linear system,Linear model,Computer science,Robustness (computer science),Artificial intelligence,Basis function,Artificial neural network,Machine learning,Recursion,Computation
Conference
ISBN
Citations 
PageRank 
3-540-61510-5
11
1.07
References 
Authors
2
1
Name
Order
Citations
PageRank
Oliver Nelles19917.27