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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
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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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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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Fuzzy inference system for osteoporosis detection
2016 IEEE Global Humanitarian Technology Conference (GHTC), 2016The goal of this paper is to propose a fuzzy inference framework for diagnosis of osteoporosis disease in the field of medical imaging. The idea behind such a framework is to assist the physician to detect, control and treat various forms of osteoporosis in better way.
Reshmalakshmi Chandrasekharan +1 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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2019
The previous two chapters explained the core concepts related to Fuzzy Logic. They discussed Fuzzy Sets and how they are different from the classical/crisp sets. You also learned about various operations that can be done on them and their properties. Then you learned about membership functions, which define the membership values of each element present
Himanshu Singh, Yunis Ahmad Lone
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The previous two chapters explained the core concepts related to Fuzzy Logic. They discussed Fuzzy Sets and how they are different from the classical/crisp sets. You also learned about various operations that can be done on them and their properties. Then you learned about membership functions, which define the membership values of each element present
Himanshu Singh, Yunis Ahmad Lone
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A proposal for an intuitionistic fuzzy inference system
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016This work describes a method to construct type-1 intuitionistic fuzzy inference systems. This type of systems is able to handle more uncertainty than a type-1 fuzzy inference system and performs faster than a type-2 fuzzy inference system. The concepts of intuitionistic membership, and intuitionistic center of area are proposed, in order to implement a
Amaury Hernandez-Aguila +2 more
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Generalized Flexible Fuzzy Inference Systems
2013 12th International Conference on Machine Learning and Applications, 2013In this paper, we propose a new variant for incremental, evolving fuzzy systems extraction from data data streams, termed as GEN-FLEXFIS (short for Generalized Flexible Fuzzy Inference Systems). It builds upon the FLEXFIS methodology (published by the authors before) and extends it for generalized Takagi-Sugeno (TS) fuzzy systems, which implement ...
Edwin Lughofer +2 more
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An inference system based on fuzzy logic
Journal of Medical Engineering & Technology, 1998We present the use of fuzzy set theory for the management of imprecision and uncertainty. We first introduced fuzzy set theory according to two different perspectives: the logical and the possibilistic/probabilistic point of view. In addition, several examples of fuzzy sets in different contexts have been considered.
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Some Fuzzy Inference Processes in Picture Fuzzy Systems
2019 11th International Conference on Knowledge and Systems Engineering (KSE), 2019Dealing with uncertain and linguistic information has been always a big problem in the areas of computational intelligence and artificial intelligence. Fuzzy inference mechanism is one of the common approaches to handling uncertain and linguistic information.
Bui Cong Cuong +3 more
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