Formalized analysis of structural characteristics of large complex systems

Macrostructural modelization is paramount to the development of large complex systems (LCS). The paper explores the macrostructural modelization of LCS in terms of a block diagram based model and a grammar based model. Firstly, the macrostructural modelization problem of LCS is formulated. Secondly, a block diagram based model is proposed and established for LCS. Specifically, two general-purpose information-processing modules are proposed and constructed, called perception cube and decision spheroid. 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. Then, a whole LCS is represented as a context-free grammar through a compounding of all level grammars. Finally, a case study is presented to demonstrate the potential usability of the proposed and established models of LCS.