Macrostructural modelization is paramount to the development of large complex systems (LCS). However, effective methodology has not been well established for macrostructural modelization of LCS. This paper explores the macrostructural modelization of LCS in terms of block diagram based model and grammar based model. Firstly, the macrostructural modelization problem of LCS is formulated. Secondly, a novel block diagram based model is proposed and established for LCS. Specifically, two novel general-purpose information-processing modules are proposed and constructed, called perception cube and decision spheroid. Through a distributed and nested structuring of the loops composed of perception cubes and decision spheroids, i.e., perception–decision links, an LCS is represented as a novel block diagram. Thirdly, a grammar based model is proposed and established for LCS through applying formal language theory to the block diagram based model. Specifically, perception cube and decision spheroid are visually represented as context-free grammars, named fusion grammar and synthesis grammar, respectively. Through a stratified constructive linkup between a stream of bottom–up growing fusion grammars and a stream of top–down growing synthesis grammars, a level of LCS is constructively defined and accordingly represented as a context-free grammar, named level grammar, for the first time. Then, a whole LCS is represented as a context-free grammar through a compounding of all level grammars. Finally, a case study on the incremental development of the computer integrated manufacturing system for a power-station boiler works is presented to demonstrate the potential usability of the proposed and established models of LCS.
|Journal||IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans|
|Publication status||Published - 1 Nov 2001|
- grammar based model
- macrostructural modelization
- large complex systems
- block diagram based model