A simple but powerful heuristic method for generating fuzzy rules from numerical data

Nozaki, Kengo; Ishibuchi, Hisao; Tanaka, Hideo

doi:10.1016/0165-0114(95)00413-0

Cited by 294 publications

(116 citation statements)

References 10 publications

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“…This is essential for avoiding overfitting to highly specific conditions which are encountered infrequently. EMCO also extracts various alternatives for surrogates based on the levels of frequency of d. Additionally, the set of rules is created at the end of the descriptor following a simple heuristic method presented in [31]. Since processing in the cloud is not constrained to service response time, we choose the heuristic solution.…”

Section: Evidence Analyzermentioning

confidence: 99%

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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Abstract-Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterising execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from GitHub.

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Section: Evidence Analyzermentioning

confidence: 99%

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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“…The first one, proposed by Wang and Mendel in [28], is a simple algorithm that, although does not obtain good accuracy results, is a traditional reference in the area. The second one, proposed by Nozaki et al in [21], uses linguistic fuzzy rules with double consequents and weights associated to them, moreover of considering an additional membership function parameter α to perform a non-linear scaling over the membership functions. The third one, proposed by Thrift in [27], is a basic GA-based learning method that only defines the fuzzy rule set.…”

Section: Experimental Studymentioning

confidence: 99%

COR Methodology: A Simple Way to Obtain Linguistic Fuzzy Models with Good Interpretability and Accuracy

Casillas

Cordón

Herrera

2003

Accuracy Improvements in Linguistic Fuzzy Modeling

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Abstract. The chapter introduces a simple learning methodology, the cooperative rules (COR) one, that improves the accuracy of linguistic fuzzy models preserving the highest interpretability. Its operation mode involves a combinatorial search of fuzzy rules performed over a set of previously generated candidate ones.The accuracy is achieved by developing a smart search space reduction and by inducing the generation of a linguistic fuzzy rule set with good cooperation. COR also ensures a good interpretability by keeping the membership functions and the model structure unaltered, as well as generating a compact rule base.

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“…In [39], Nozaki et al presented a heuristic method for generating T-S fuzzy rules from numerical data, and then converted the consequent parts of T-S fuzzy rules into linguistic representation.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Takagi‐Sugeno fuzzy model for the estimation of nonlinear functions

Jiménez

Al‐Hadithi

Matı́a

et al. 2010

Asian Journal of Control

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Two new and efficient approaches are presented to improve the local and global estimation of the Takagi-Sugeno (T-S) fuzzy model. The main aim is to obtain high function approximation accuracy and fast convergence. The main problem is that the T-S identification method can not be applied when the membership functions are overlapped by pairs. The approaches developed here can be considered as generalized versions of T-S method with optimized performance. The first uses the minimum norm approach to search for an exact optimum solution at the expense of increasing complexity and computational cost. The second is a simple and less computational method, based on weighting of parameters. Illustrative examples are chosen to evalúate the potential, simplicity and remarkable performance of the proposed methods and the high accuracy obtained in comparison with the original T-S model.

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A simple but powerful heuristic method for generating fuzzy rules from numerical data

Cited by 294 publications

References 10 publications

Evidence-Aware Mobile Computational Offloading

Evidence-Aware Mobile Computational Offloading

COR Methodology: A Simple Way to Obtain Linguistic Fuzzy Models with Good Interpretability and Accuracy

Improvement of Takagi‐Sugeno fuzzy model for the estimation of nonlinear functions

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