No abstract

PurposeFuzzy theory provides a rigorous, flexible approach to the problem of defining and computing. Therefore, to facilitate decision making in a geographic information system (GIS), the graph layer indicator and the Takagi‐Sugeno (T‐S) fuzzy model must be integrated. This study aims to explain several versions of the T‐S fuzzy model based on fuzzy theory and fuzzy operation.Design/methodology/approachAn inference model is constructed for GIS using the T‐S fuzzy model to formulate an integrated T‐S decision‐making (TSDMK) system.FindingsThe TSDMK system accommodates inexact, linguistic, vague and uncertain GIS data. The operator assigns most graph layer indicators by intuition.Practical implicationsSimulation results for the Hualien main station show that the proposed TSDMK system is an effective approach for GIS decision making.Originality/valueThis investigation assesses applications of fuzzy logic for decision making in a GIS based on TSDMK graphs focusing on model‐based systems.

The least square method is in generally used for curve fitting problems. We here propose a fuzzy S-curve regression model to deal with the case in which the observed data are given by fuzzy numbers. The fuzzy regression curve, obtained for project control and predicting the progress of large-scale or small-scale engineering, is smoothly connected by a Takagi-Sugeno (T-S) fuzzy model. This paper also proposes the concept that the upper bound and lower bound are given instead of the confidence interval when the observed data are not obtained exactly. Based on the project cash flow and progress payment records of an example project taken from the Department of Rapid Transit Systems, Taipei City Government, this model is demonstrated and tentative conclusions concerning the model are given. The S-curve equation developed here could be used in a variety of applications related to project control for the management of working capital for construction firms.

Purpose -This study aims to investigate the relationship between structural damage and sensitivity indices using the Hilbert-Huang transform (HHT) method. Design/methodology/approach -The relationship between structural damage and the sensitivity indices is obtained by using the HHT method. Three sensitivity indices are proposed: the ratio of rotation (RR), the ratio of shifting value (SV) and the ratio of bandwidth (RB). The nonlinear single degree of freedom and multiple degree of freedom models with various predominant frequencies are constructed using the SAP2000 program. Adjusted PGA El Centro and Chi-Chi (TCU068) earthquake data are used as the excitations. Next, the sensitivity indices obtained using the HHT and the fast Fourier transform (FFT) methods are evaluated separately based on the acceleration responses of the roof structures to earthquakes. Findings -Simulation results indicate that, when RR < 1, the structural response is in the elastic region, and neither the RB nor SV in the HHT and FFT spectra change. When the structural response is nonlinear, i.e. RR1, a positive trend of change occurs in RB and RR, while in the HHT spectra, SV increases with an increasing RR. Moreover, the FFT spectra reveal that SV changes only when the RR is sufficiently large. No steady relationship between the RB and the RR can be found. Originality/value -The paper demonstrates the effectiveness of the HHT method.

This paper proposes a fuzzy Lyapunov method for stability analysis of nonlinear systems represented by Tagagi-Sugeno (T-S) fuzzy model. The fuzzy Lyapunov function is defined in fuzzy blending quadratic Lyapunov functions. Based on fuzzy Lyapunov functions, some stability conditions are derived to ensure nonlinear systems are asymptotic stable. By using parallel distributed compensation (PDC) scheme, we design a nonlinear fuzzy controller for the nonlinear system. This control problem will be reformulated into linear matrix inequalities (LMI) problem.