The investigation of the effects of cracks or similar weaknesses on the load carrying capacity of structural elements such as columns, beams and shells is an important problem in civil, mechanical, earthquake and aerospace engineering. In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)model have been successfully used for the buckling analysis of sl...

Original scientific paper This paper proposes a Neuro-fuzzy system for quantitative assessment of the effects of intelligent transportation systems and technologies on road fatalities. The basic idea in developing Neuro-fuzzy system is the fact that intelligent transportation systems and technologies activate some safety mechanisms and in turn the activation of safety mechanisms will have a pos...

The late detection of the kick (the entrance of underground fluids into oil wells) leads to oil well blowouts. It causes human life loss and imposes a great deal of expenses on the petroleum industry. This paper presents the application of adaptive neuro-fuzzy inference system designed for an earlier kick detection using measurable drilling parameters. In order to generate the initial fuzzy inf...

Wave height as well as water depth at the breaking point are two basic parameters which are necessary for studying coastal processes. In this study, the application of soft computing-based methods such as artificial neural network (ANN), fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS) and semi-empirical models for prediction of these parameters are investigated. Th...

Robust parameter design has been successfully applied to a variety of engineering problems for enhancing the robustness of the system; however, it cannot deal with multiple dynamic quality systems. Although several other approaches have been presented to resolve this problem, they are unable to efficiently treat the situations that the control factors have continuous values. This study incorpor...

In this paper, the effect of possible missing data on wind power estimation is examined. One−month wind speed data obtained from wind and solar observation station which is constructed at Iki Eylul Campus of Anadolu University is used. A closed correlation is found between consecutive wind speed data that are collected for a period of 15 second. A very short time wind speed forecasting model is...

One of the most popular data mining areas, which estimate future trends of data, is classification. This research is dedicated to predict Iranian passenger train delay with high accuracy over Iranian railway network. A hybrid method based on neuro-fuzzy inference system and Two-step clustering is used for this purpose. The results indicate that the hybrid method is superior over the other commo...

— In this paper a new approach to model climatic variations in the Plio-Pleistocene is presented. In a recent reference, Rial in [1] introduced the working hypothesis that frequency modulation (FM) of the orbital eccentricity forcing may be an important source of the nonlinearities observed in the δO time series from deep-sea sediment cores. Two models are proposed based on the ANFIS (Adaptive ...

This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy ...

The lane change maneuver is among the most popular driving behaviors. It is also the basic element of important maneuvers like overtaking maneuver. Therefore, it is chosen as the focus of this study and novel multi-input multi-output adaptive neuro-fuzzy inference system models (MANFIS) are proposed for this behavior. These models are able to simulate and predict the future behavior of a Driver...