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Credibility in Fuzzy Inference Systems
Cybernetics and Systems Analysis, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Provotar, O. I., Provotar, O. O.
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A fuzzy inference system for power systems
2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), 2017The amount of data available on the industrial plants has seen an exponential growth in the last few years. One of the greatest player in this trend has been the technological progress that has made also cheap devices smart. Thus, also the low voltage (LV) circuit breaker (CB) becomes an acquisition system able to communicate data to the cloud. Because
Carboni, Alberto +2 more
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Z-Adaptive Fuzzy Inference Systems
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021Z-numbers consist of two components, restriction and restriction reliability, to cover both possibilistic and probabilistic uncertainties. So far, the components of Z-numbers are merely determined by expert knowledge and lack automated learning/training.
Fatemeh Rezaee-Ahmadi +2 more
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2019
Liver fibrosis is the natural wound-healing response to parenchymal injury in chronic liver diseases. It may eventually results in liver Cirrhosis (F4) and its various complications (F0–F1, F2, F3). The assessment of liver fibrosis staging is essential for people suffering from chronic liver diseases.
Joey Sing Yee Tan, Amandeep S. Sidhu
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Liver fibrosis is the natural wound-healing response to parenchymal injury in chronic liver diseases. It may eventually results in liver Cirrhosis (F4) and its various complications (F0–F1, F2, F3). The assessment of liver fibrosis staging is essential for people suffering from chronic liver diseases.
Joey Sing Yee Tan, Amandeep S. Sidhu
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Fuzzy Inference Network with Mamdani Fuzzy Inference System
2018In the modern era, the amount of data generated is increasing at an exponential rate. The generated data has both numeric as well as linguistic form. Learning or extracting relevant information from these types of data is a major challenge for researchers.
Nishchal K. Verma +3 more
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International Journal of Fuzzy System Applications, 2015
One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS).
Md Mahfuzur Rahman Siddiquee +2 more
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One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS).
Md Mahfuzur Rahman Siddiquee +2 more
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On Liu’s Inference Rules for Fuzzy Inference Systems
2010Liu’s inference is a process of deriving consequences from fuzzy knowledge or evidence via the tool of conditional credibility. Using membership functions, this paper derives some expressions of Liu’s inference rule for fuzzy systems. This paper also gives some new inference rules with multiple antecedents and with multiple if-then rules.
Xin Gao, Dan A. Ralescu, Yuan Gao 0021
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A Fuzzy Inference System for Meta-Evaluation
Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007), 2007This paper presents a new methodology for meta-evaluation that makes use of fuzzy sets and fuzzy logic concepts. It comprehends a data collection instrument and a hierarchical fuzzy inference system. The advantages of the proposed system are: (i) the instrument, which allows intermediate answers; (ii) the inference process ability to adapt to specific ...
Ana Carolina Letichevsky +2 more
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A fuzzy inference system for sleep staging
2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), 2011In this paper, a fuzzy inference system for sleep staging was developed. Nine input variables including temporal and spectrum analyses of the EEG, EOG, and EMG signals were extracted and normalization was applied to these variables to reduce the effect of individual variability.
Sheng-Fu Liang +4 more
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Autotuning of Fuzzy Inference System with RL
2007 American Control Conference, 2007Reinforcement learning refers to a class of learning tasks and algorithms in which the learning system learns an associative mapping by maximizing a scalar evaluation function by interacting with environment. Fuzzy actor critic learning (FACL) is a reinforcement learning method based on dynamic programming principle.
Natarajan Pappa, S. Rama Krishnan
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