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Can I read Matrix Scaling for Large-scale System Decomposition on EtoBox?
Matrix Scaling for Large-scale System Decomposition by John D. Finney; Bonnie S. Heck is a Engineering article available to read on EtoBox.
What is Matrix Scaling for Large-scale System Decomposition about?
Many large-scale systems exhibit the structure of weakly connected components. In such cases, proper identification of weakly coupled subsystems will add insight into large-scale system behavior, and aid in related tasks such as the design of decentralized control. e-decomposition is a well-known efficient graph-theoretic algorithm for achieving a complete set of nested decompositions of a large-scale system. This paper shows how system matrix scaling can affect these decompositions, and determines that a system is properly scaled for e-decomposition when it is max-balanced, a property associated with weighted directed graphs. Also, it is shown that an existing algorithm for max-balancing can be altered slightly to return the complete set of Edecompositions, thus removing the need for two separate algorithms. Finally, the advantages of max-balancing before decomposition are shown for the application of decentralized control subsystem identification.
Who reads Matrix Scaling for Large-scale System Decomposition?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- John D. Finney; Bonnie S. Heck
- Publisher
- Elsevier Science; Elsevier ; Elsevier Ltd.; Test accounts; Elsevier BV (ISSN 0005-1098)
- Published
- 1996
- Language
- EN
- Field
- Engineering (Physical Sciences)