Systems science 

About: Systems science is an academic journal. The journal publishes majorly in the area(s): Nonlinear system & Linear system. It has an ISSN identifier of 0137-1223. Over the lifetime, 327 publications have been published receiving 1320 citations. The journal is also known as: systems theory.

A graphical tabular model for rule-based logic programming and verification

Abstract: New trends in development of databases and expert systems seems to underline the role of graphical specification tools, visual information modeling and formal verification procedures. This paper incorporates these new ideas and, moreover, tries to present putting them in engineering practice. The main goal is to move the design procedure to a more abstract, logical level, where knowledge specification is based on use of abstract rule representation, called eXtended Tabular Trees. The main idea behind XTT is to build a hierarchy of ObjectAttribute-Value Tables (OAV table). The basic component for knowledge specification is an OAV table. It is analogous to a relational database table; however, it contains conditional part and decision columns. Moreover, the attribute values can be non-atomic ones. Each row provides specification of a single rule. The OAV tables can be connected with one another through appropriate links specifying the control flow in the system. The design specification is automatically translated into Prolog code, so the designer can focus on logical specification of safety and reliability. On the other hand, formal aspects such as completeness, determinism, etc. are automatically verified on-line during the design, so that it verifiable characteristics are preserved. From practical point of view, the design process is performed with a intelligent tool named Mirella.

New methods for the identification of nonlinear model structures based upon genetic programming techniques

Abstract: Identifying nonlinear model structures as a part of analyzing a physical system means trying to generate an algebraic expression as a part of an equation that describes the physical representation of a dynamic system. Many existing system identification methods are based on parameter identification. In this paper we describe a method using genetic programming to evolve an algebraic representation of measured input-output response data. The main advantage of the presented approach is that unlike many other identification methods, it does not restrict the set of models that can be identified but can be applied to any kind of data sets representing a system's observed or simulated input and output signals. This paper describes research that was done for the project "Specification, Design and Implementation of a Genetic Programming Approach for Identifying Nonlinear Models of Mechatronic Systems". The goal of the project is to find models for mechatronic systems; our task was to examine, whether the methods of Genetic Programming are suitable for determining the structures of physical systems by analyzing a system’s measured behaviour or not.