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(searched for: doi:10.13176/11.125)
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, Ren Diao,
IEEE Transactions on Fuzzy Systems, Volume 26, pp 1878-1892; https://doi.org/10.1109/tfuzz.2017.2755000

Abstract:
Fuzzy rule interpolation (FRI) offers an effective approach for making inference possible in sparse rule-based systems (and also for reducing the complexity of fuzzy models). However, requirements of fuzzy systems may change over time and hence, the use of a static rule base may affect the accuracy of FRI applications. Fortunately, an FRI system in action will produce interpolated rules in abundance during the interpolative reasoning process. Whilst such interpolated results are discarded in existing FRI systems, they can be utilised to facilitate the development of a dynamic rule base in supporting subsequent inference. This is because the otherwise relinquished interpolated rules may contain possibly valuable information, covering regions that were uncovered by the original sparse rule base. This paper presents a dynamic fuzzy rule interpolation (D-FRI) approach by exploiting such interpolated rules in order to improve the overall systems coverage and efficacy. The resulting D-FRI system is able to select, combine, and generalise informative, frequently used interpolated rules for merging with the existing rule base while performing interpolative reasoning. Systematic experimental investigations demonstrate that D-FRI outperforms conventional FRI techniques, with increased accuracy and robust- ness. Furthermore, D-FRI is herein applied for network security analysis, in devising a dynamic intrusion detection system (IDS) through integration with the Snort software, one of the most popular open source IDSs. This integration, denoted as D-FRI- Snort hereafter, delivers an extra amount of intelligence to predict the level of potential threats. Experimental results show that with the inclusion of a dynamic rule base, by generalising newly interpolated rules based on the current network traffic conditions, D-FRI-Snort helps reduce both false positives and false negatives in intrusion detection.
Nitin Naik, Ren Diao,
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) pp 1-8; https://doi.org/10.1109/fuzz-ieee.2015.7338026

Abstract:
Fuzzy rule interpolation (FRI) has been a vital reasoning tool for sparse fuzzy rule-based systems. Throughout interpolative reasoning, an FRI system may produce a large number of interpolated rules, which generally serve no further purpose once the required outcomes have been obtained. However, this abandoned pool of interpolated rules can be used to improve the existing sparse rule base, because they contain useful information on the underlying problem domain. Efficient extraction of knowledge from such a pool of interpolated rules are indeed helpful to analyse and update the sparse rule base, leading to a dynamic sparse fuzzy rule base for building an enhanced fuzzy system. Following this idea, a genetic algorithm (GA) based dynamic fuzzy rule interpolation framework has been proposed recently. This paper presents an extension of the dynamic FRI system. In particular, it investigates different fitness functions and their effects on the outcomes of the GA-based system. A variety of fitness functions based on cluster quality indices are employed and tested, including Dunn Index, Davies-Boulding Index, Ball-Hall Index and BetaCV Index. Experimental investigation demonstrates that results obtained by the use of Dunn index or Davies-Bouldin index are better than those by Ball-Hall or BetaCV index, with those using Davies-Bouldin index performing the best overall. Such results offer an empirical guideline for the selection of the fitness function in implementing accurate GA-based dynamic FRI systems.
Nitin Naik, Ren Diao,
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) pp 2198-2205; https://doi.org/10.1109/fuzz-ieee.2014.6891816

Abstract:
Fuzzy rule interpolation (FRI) is a well established area for reducing the complexity of fuzzy models and for making inference possible in sparse rule-based systems. Regardless of the actual FRI approach employed, the interpolative reasoning process generally produces a large number of interpolated rules, which are then discarded as soon as the required outcomes have been obtained. However, these interpolated rules may contain potentially useful information, e.g., covering regions that were uncovered by the original sparse rule base. Thus, such rules should be exploited in order to develop a dynamic rule base for improving the overall system coverage and efficacy. This paper presents a genetic algorithm based dynamic fuzzy rule interpolation framework, for the purpose of selecting, combining, and promoting informative, frequently used intermediate rules into the existing rule base. Simulations are employed to demonstrate the proposed method, showing better accuracy and robustness than that achievable through conventional FRI that uses just the original sparse rule base.
Xiaohong Kong, Hong Shen, Xiqu Chen, Chao Wang, Changyuan Song
2010 International Conference On Computer Design and Applications, Volume 2; https://doi.org/10.1109/iccda.2010.5541410

Abstract:
In this paper, a self-adaptive algorithm based on Tabu Search (TS) is proposed to minimize the makespan, which is suitable to the grid dynamic characteristic. The process of scheduling is divided into partial scheduling, and last partial information is exploited to decide the next partial scheduling parameters set, which is self-adaptive to the stochastic environment. The algorithm was simulated in GridSim toolkit and the results demonstrated that the proposed algorithm improved performance compared to existing scheduling techniques.
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