fuzzy inference system (fis)

Uncertain and stochastic conditions of accidents could affect the risk and complexity of decisions for managers. Accident prediction methods could be helpful to confront these challenges. Fuzzy inference systems (FIS) have developed a new attitude in this field in recent years. As lift truck accidents are one of the main challenges that industries face worldwide, this paper focuses on predicting the possibility of these types of accidents. At first, the data collection is done by using interviews, questionnaires, and surveys. A FIS approach is proposed to predict the possibility of lift truck accidents in industrial plants. Furthermore, our approach is validated using data from many real cases. The results are approved by the multivariate logistic regression method. Finally, the output of the fuzzy and logit models is compared with each other. The re-validation of the fuzzy control model and high consistent of the output of these two models is presented.

Internet of Things (IoT) and cloud computing technologies have connected the infrastructure of the city to make the context-aware and more intelligent city for utility its major resources. These technologies have much potential to solve thechallenges of urban areas around the globe to facilitate the citizens. A framework model that enables the integration of sensor’s data and analysis of the data in the context of smart parking is proposed. These technologies use sensors anddevices deployed around the city parking areas sending real time data through the edge computers to the main cloud servers. Mobil-Apps are developed that used real time data, set from servers of the parking facilities in the city. Fuzzification is shown to be a capable mathematical approach for modeling city parking issues. To solve the city parking problems in cities a detailed analysis of fuzzy logic proposed systems is developed. This paper presents the resultsachieved using Mamdani Fuzzy Inference System to model complex smart parking system. These results are verified using MATLAB simulation.

Evaluating indicators such as productivity and efficiency, make managers aware at various levels of the strengths and weaknesses of the organization. In today''s business environment, organizations try to improve their efficiency in order to have more power in the global competition. The present paper presents an integrated fuzzy approach for the efficiency evaluation of industrial co-operatives. This method consists of three different techniques: (1) identify important evaluation criteria based on Balanced Scorecard (BSC) According to the previous literature review and expert preference; (2) apply fuzzy inference system (FIS) to eliminate unsuitable criterion; and, (3) develop data envelopment analysis (DEA) approach for the final evaluation. Among the indicators «The capacity of machines and production to capacity ratio» determined as the input parameters and «annual profits, customer satisfaction and continuous improvement of productivity» were considered as the output of the proposed DEA model. Finally, an actual industrial application is presented to demonstrate the proposed method. By transforming the linguistic variables to quantitative numbers by fuzzy numbers, the DEA model was formed and four units of the fifteen cooperatives were identified as efficient units..

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 inference system, subtractive clustering is utilized. The training set contains 50 data samples and there are 362 data samples for testing the proposed method. Also, ANFIS structure is examined at different radii (the parameter of subtractive clustering). Different conformations are tested to get the earliest detection and the lowest false alarms while facing kick. Eventually, ANFIS verifies the danger exposure depth of about 28.6 meters before the depth that the kick was sensed by crew. Such an assessment gives the rig crew enough time to prepare for the danger and stop the operation before being exposed to high pressure zones.