System identification

About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.

A numerical approach to genetic programming for system identification

Abstract: This paper introduces a new approach to genetic programming (GP), based on a numerical technique, which integrates a GP-based adaptive search of tree structures, and a local parameter tuning mechanism employing statistical search (a system identification technique). In traditional GP, recombination can cause frequent disruption of building blocks or mutation can cause abrupt changes in the semantics. To overcome these difficulties, we supplement traditional GP with a local hill-climbing search, using a parameter tuning procedure. More precisely, we integrate the structural search of traditional GP with a multiple regression analysis method and establish our adaptive program, called STROGANOFF (STructured Representation On Genetic Algorithms for NOn-linear Function Fitting). The fitness evaluation is based on a minimum description length (MDL) criterion, which effectively controls the tree growth in GP. We demonstrate its effectiveness by solving several system identification (numerical) problems and compare the performance of STROGANOFF with traditional GP and another standard technique (radial basis functions). We then extend STROGANOFF to symbolic (nonnumerical) reasoning by introducing multiple types of nodes, using a modified MDL-based selection criterion and a pruning of the resultant trees. The effectiveness of this numerical approach to GP is demonstrated by successful application to symbolic regression problems.

Hybrid Time‐Frequency Blind Source Separation Towards Ambient System Identification of Structures

Abstract: This article will discuss how ambient system identification in noisy environments, in the presence of low-energy modes or closely-spaced modes, is a challenging task. Conventional blind source separation techniques such as second-order blind identification (SOBI) and Independent Component Analysis (ICA) do not perform satisfactorily under these conditions. Furthermore, structural system identification for flexible structures require the extraction of more modes than the available number of independent sensor measurements. This results in the estimation of a non-square modal matrix that is spatially sparse. To overcome these challenges, methods that integrate blind identification with time-frequency decomposition of signals have been previously presented. The basic idea of these methods is to exploit the resolution and sparsity provided by time-frequency decomposition of signals, while retaining the advantages of second-order source separation methods. These hybrid methods integrate two powerful time-frequency decompositions—wavelet transforms and empirical mode decomposition—into the framework of SOBI. In the first case, the measurements are transformed into the time-frequency domain, followed by the identification using a SOBI-based method in the transformed domain. In the second case, a subset of the operations are performed in the transformed domain, while the remaining procedure is conducted using the traditional SOBI method. A new method to address the under-determined case arising from sparse measurements is proposed. Each of these methods serve to address a particular situation: closely-spaced modes or low-energy modes. The proposed methods are verified by applying them to extract the modal information of an airport control tower structure located in Canada.