Use Of Fuzzy Inference Systems Research Articles

Impact of highway design on traffic safety: How geometric elements affect accident risk

The study conducted in India using Fuzzy Inference System (FIS) is a novel approach to predicting traffic accident rates on rural highways. By using various highway geometric elements as input data, the study was able to identify risk variables related to roadway features and predict accident rates using FIS. The findings of the study suggest that FIS is a valuable tool for predicting accident rates and can help identify the factors that contribute to accidents. The study also found statistically significant positive connections between geometric elements and accident rates, which highlights the importance of highway design and safety measures. The use of Fuzzy Inference Systems in accident prediction is a promising approach as it allows for a more comprehensive understanding of the complex relationships between various factors contributing to accidents. The model was tested using simulation and data analysis and was found to fit well with real-world data, indicating its potential for practical applications in road safety management. Overall, this study provides important insights into the use of FIS in predicting accident rates on rural highways and can help guide future research in this area. It also highlights the importance of considering various highway geometric elements in designing safer highways and implementing appropriate safety measures to reduce accident rates. However, the road accidents can have significant impacts on ecological cycles, including habitat fragmentation, wildlife mortality, pollution, and climate change.

Modelo de sistema de inferência Fuzzy baseado em matrizes de probabilidade e impacto para classificar riscos em projetos

Projects are essential for organizations to implement their strategies and the success of the projects is crucial for organizations. However, in a context of uncertainties, it is necessary to address risks that threaten this success, allocating resources for its prevention. To decide how to prioritize them and allocate resources, Probability and Impact Matrices (MPIs)are used. MPIs are built with the employment of a human expert, usually the project manager. Although popular, MPIs may have shortcomings, for example, analyzing inaccurate information from the human expert's knowledge and the inability to rank characteristic risks when using discrete classes. Thus, the use of Fuzzy Inference Systems (SIF) in an MPI would allow to address these deficiencies by modeling human knowledge reducing its uncertainty. A general objective of this work was to develop a Fuzzy Inference System based on MPIs to classify risks in projects. In order to reach this objective, we conducted experiments in five stages: (I) Generation of the database containing probability, impact and risk values, varying the correlation between probability-impact, (II) Classification of MPI Conventional and MPI proposed by Cox (2008), (III) Implementation of SIF based on Phase II MPIs rules, (IV) Application of SIF and (V) Analysis of results. It was verified that the SIF model is advantageous, compared to the conventional MPI, allowing the classification of the risks in a continuous scale, which facilitates the prioritization and allocation of resources and the reduction of uncertainty allowing to classify risks in projects.

An Integrated Mathematical Model for Supplier Selection

Extensive research has been conducted on supplier evaluation and selection as a strategic and crucial component of supply chain management in recent years. However, few articles in the previous literature have been dedicated to the use of fuzzy inference systems as an aid in decision-making. Therefore, this essay attempts to demonstrate the application of this method in evaluating suppliers, based on a comprehensive framework of qualitative and quantitative factors besides the effect of gradual coverage distance. The purpose of this study is to investigate the applicability of the numerous measures and metrics in a multi-objective optimization problem of the supply chain network design with the aim of managing the allocation of orders by coordinating the production lines to satisfy customers’ demand. This work presents a dynamic non-linear programming model that examines the important aspects of the strategic planning of the manufacturing in supply chain. The effectiveness of the configured network is illustrated using a sample, following which an exact method is used to solve this multi-objective problem and confirm the validity of the model, and finally the results will be discussed and analyzed.

Fuzzy-Based Uplink Power Control in Forward Broadband Satellite Links

End-to-end performance of broadband satellite links operating at Ku band and above is studied in this paper. The problem of power control in terms of optimal power allocation and maximization of the total carrier-to-interference-and-noise ratio is considered. More specifically, the paper proposes the use of Fuzzy Inference Systems (FIS) to optimize power allocation in a dual-hop (end-to-end) satellite system, considering the general case of dual-polarized satellite links with transparent satellite transponder. The quality of the link is assessed through the FIS based on the predicted values of rain attenuation induced on the corresponding links and the interference-to-noise ratios. According to this estimation, the satellite hub-station increases or reduces the allocated uplink power. The proposed scheme’s effectiveness is investigated in terms of carrier-to-noise plus depolarization plus interference ratio and power consumption for 30/20 GHz frequency in the uplink and the downlink respectively. A variety of conditions is considered regarding the rain attenuation values from 0 (no rain) to 30 dB and interference-to-noise ratio from −15 to 15 dB to both links. Finally, the scheme is compared to step-based power control algorithms, showing that it can significantly reduce the total consumed power.

Review of the application of fuzzy inference systems in river flow forecasting

This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi–Sugeno–Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective tools for river flow forecasting, even though their application is rather limited in comparison to the popularity of neural networks models. In addition to this, it was found that there are several unresolved issues requiring further attention before more clear guidelines for the application of fuzzy inference systems can be given.

Estimating the uniaxial compressive strength of some clay‐bearing rocks selected from Turkey by nonlinear multivariable regression and rule‐based fuzzy models

Abstract: Although the use of predictive models in rock engineering and engineering geology is an important issue, some simple and multivariate regression techniques traditionally employed in these areas have recently been challenged by the use of fuzzy inference systems and artificial neural networks. The purpose of this study was to construct some predictive models to estimate the uniaxial compressive strength of some clay‐bearing rocks, depending on examination of their slake durability indices and clay contents. For this purpose, the simple and nonlinear multivariable regression techniques and the Mamdani fuzzy algorithm are compared in terms of their accuracy. To increase the accuracy of the Mamdani fuzzy inference system, the weighted if–then rules are extracted. To compare the predictive performances of the models, the statistical performance indices (root mean square error and variance account for) are calculated and the results are discussed. The indices reveal that the fuzzy inference system has a slightly higher prediction capacity than the regression models. The basic reason for the higher performance of the fuzzy inference system is the flexibility of the fuzzy approach.

An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment

This paper describes a first effort to design and implement an adaptive neuro-fuzzy inference system-based approach to estimate prices for residential properties. The data set consists of historic sales of houses in a market in the Midwest region of the United States and it contains parameters describing typical residential property features and the actual sale price. The study explores the use of fuzzy inference systems to assess real estate property values and the use of neural networks in creating and fine-tuning the fuzzy rules used in the fuzzy inference system. The results are compared with those obtained using a traditional multiple regression model. The paper also describes possible future research in this area.

Studying the Use of Fuzzy Inference Systems for Motor Imagery Classification

This paper studies the use of fuzzy inference systems (FISs) for motor imagery classification in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The results of the four studies achieved are promising as, on the analysed data, the used FIS was efficient, interpretable, showed good capabilities of rejecting outliers and offered the possibility of using a priori knowledge.

Spatial decisions under uncertainty: fuzzy inference in urban water management

The endogenous complexity and spatial nature of the problems encountered in the urban water management environment present decision-makers with three major problems: (a) in the urban environment, every decision is site-specific, almost on a case-by-case basis, (b) the decision-maker must access, simultaneously, a large amount of information, increasing with rising spatial resolution and (c) the information to be evaluated is heterogeneous, including engineering, economical and social characteristics and constraints. The first two problems indicate that urban water management is an ideal field to develop and use spatial decision support systems (SDSS), while the latter promotes the use of fuzzy inference systems as a key mathematical framework. This research discusses the nature of uncertainty in environmental management in general and urban water management in particular, argues that fuzzy, rule-based, inference systems can be an invaluable tool for uncertainty quantification and presents the relevant elements of a prototype SDSS for urban water management. The examples presented in this paper are based on an application of the SDSS in water demand management.

Computation of the resonant frequency of electrically thin and thick rectangular microstrip antennas with the use of fuzzy inference systems

Computation of the resonant frequency of electrically thin and thick rectangular microstrip antennas with the use of fuzzy inference systems