<i>Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems</i>

Chen, Guanrong; Pham, Trung Tat; Boustany, NM

doi:10.1115/1.1421114

Cited by 180 publications

(58 citation statements)

References 7 publications

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“…One proposed way to account for uncertainty in success or failure at first insemination is via a threshold model using fuzzy logic classification, where fuzzy logic classification uses imprecise propositions based on fuzzy set theory to assign partial membership of a set [2]. Therefore, the objective of the current study was to evaluate different methods of analyzing uncertain binary responses with application to success or failure at first insemination in beef cattle.…”

Section: Introductionmentioning

confidence: 99%

A simulation study for the analysis of uncertain binary responses: Application to first insemination success in beef cattle

et al. 2005

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-A simulation was carried out to investigate the methods of analyzing uncertain binary responses for success or failure at first insemination. A linear mixed model that included, herd, year, and month of mating as fixed effects; and unrelated service sire, sire and residual as random effects was used to generate binary data. Binary responses were assigned using the difference between days to calving and average gestation length. Females deviating from average gestation length lead to uncertain binary responses. Thus, the methods investigated were the following: (1) a threshold model fitted to certain (no uncertainty) binary data (M1); (2) a threshold model fitted to uncertain binary data ignoring uncertainty (M2); and (3) analysis of uncertain binary data, accounting for uncertainty from day 16 to 26 (M3) or from day 14 to 28 (M4) after introduction of the bull, using a threshold model with fuzzy logic classification. There was virtually no difference between point estimates obtained from M1, M3, and M4 with true values. When uncertain binary data were analyzed ignoring uncertainty (M2), sire variance and heritability were underestimated by 22 and 24%, respectively. Thus, for noisy binary data, a threshold model contemplating uncertainty is needed to avoid bias when estimating genetic parameters.binary data / fertility / fuzzy logic / simulation / threshold model

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Section: Introductionmentioning

confidence: 99%

A simulation study for the analysis of uncertain binary responses: Application to first insemination success in beef cattle

et al. 2005

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“…In principle the idea of fuzzy inference systems is that at every point in time and for a unique set of input parameter values, multiple rules can be triggered with a different degree of truth (strength) and their individual outputs are then "combined" to derive a unique crisp output value. Fuzzy inference systems offer robustness and smooth reaction [17] however they do require the existence of an expert to define the appropriate rule-set. The main challenge is therefore to be able to generate the appropriate rule-set without the existence of a direct trainer.…”

Section: Fuzzy Q-learning Conceptsmentioning

confidence: 99%

“…see [15,16] to introduce autonomic capabilities in network control systems, and is a combination of fuzzy logic [17] with Q-learning (type of Reinforcement Learning (RL)) [18] that aims to combine the robustness of a rule based fuzzy system with the adaptability and learning capabilities of Q-learning. In this Section we highlight the main concepts and benefits of this approach and its applicability in the context of PCN-based AC.…”

Section: Fuzzy Q-learning Pcn-based Admission Controlmentioning

confidence: 99%

A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control

Georgoulas

Moessner

Mansour

et al. 2012

Dependable Networks and Services

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Abstract. Admission control aims to compensate for the inability of slowchanging network configurations to react rapidly enough to load fluctuations. Even though many admission control approaches exist, most of them suffer from the fact that they are based on some very rigid assumptions about the perflow and aggregate underlying traffic models, requiring manual reconfiguration of their parameters in a "trial and error" fashion when these original assumptions stop being valid. In this paper we present a fuzzy reinforcement learning admission control approach based on the increasingly popular Pre-Congestion Notification framework that requires no a priori knowledge about traffic flow characteristics, traffic models and flow dynamics. By means of simulations we show that the scheme can perform well under a variety of traffic and load conditions and adapt its behavior accordingly without requiring any overly complicated operations and with no need for manual and frequent reconfigurations.

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“…Fuzzy rule is based on classical implication and inference rules [13]. The difference between classical reasoning rule [14] and fuzzy rule [13] is that x and y values denote fuzzy values in fuzzy rules whereas they represent Boolean value in classical reasoning rules.…”

Section: Extension Of Fuzzy Rule To Semantic Fuzzy Rulementioning

confidence: 99%

Training-Free Fuzzy Logic Based Human Activity Recognition

Kim¹,

Helal²

2014

Journal of Information Processing Systems

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Abstract-The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other trainingbased approaches.

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Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems

Cited by 180 publications

References 7 publications

A simulation study for the analysis of uncertain binary responses: Application to first insemination success in beef cattle

A simulation study for the analysis of uncertain binary responses: Application to first insemination success in beef cattle

A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control

Training-Free Fuzzy Logic Based Human Activity Recognition

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