System identification in large scale systems with hierarchical structures

Abstract A large-scale machine system often has a general hierarchical structure. For hierarchical structures, optimization is difficult because many local optima almost always arise, however genetic algorithms that have a hierarchical genotype can be applied to treat such problems directly. Relations between the structural components are analyzed and this information is used to partition the hierarchical structure. Partitioning large-scale problems into sub-problems that can be solved using parallel processed GAs increases the efficiency of the optimization search. The optimization of large-scale systems then becomes possible due to information sharing of Pareto optimum solutions for the sub-problems.

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Hierarchical Parallel Processes of Genetic Algorithms for Design Optimization of Large-Scale Products

Journal of Mechanical Design ◽

10.1115/1.1666889 ◽

2004 ◽

Vol 126 (2) ◽

pp. 217-224 ◽

Cited By ~ 12

Author(s):

Masataka Yoshimura ◽

Kazuhiro Izui

Keyword(s):

Genetic Algorithms ◽

Hierarchical Structure ◽

Large Scale ◽

Hierarchical Structures ◽

Pareto Optimum ◽

Local Optima ◽

Large Scale Systems ◽

Parallel Processes ◽

The Hierarchical Structure ◽

Optimization Search

A large-scale machine system often has a general hierarchical structure. For hierarchical structures, optimization is difficult because many local optima almost always arise, however genetic algorithms that have a hierarchical genotype can be applied to treat such problems directly. Relations between the structural components are analyzed and this information is used to partition the hierarchical structure. Partitioning large-scale problems into sub-problems that can be solved using parallel processed GAs increases the efficiency of the optimization search. The optimization of large-scale systems then becomes possible due to information sharing of Pareto optimum solutions for the sub-problems.

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Hierarchical identification of large-scale system models

10.51415/10321/2716 ◽

1997 ◽

Author(s):

◽

Boris R. Jankovic

Keyword(s):

System Identification ◽

Large Scale ◽

Complex Problem ◽

Hierarchical Identification ◽

Large Scale Systems ◽

Large Scale System ◽

System Models ◽

The One

In this study we propose a new concept and methodology of hierarchical identification. The need for such a methodology comes from the fact that identification of large-scale systems (LSSs) by one-shot approach may be numerically very complex. The analysis of LSSs is, in general, not approached by the one-shot methodologies normally associated with non-LSSs. The proposed method of hierarchical identification can be therefore viewed as an extension of LSS methodologies to system identification. LSS methodology aims at breaking up the initial, complex problem into a set of smaller size subproblems.

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