Strategies to Identify Fuzzy Rules Directly From Certainty Degrees: A Comparison and a Proposal

Carmona, Pablo; Castro, Jorge; Zurita, J. M.

doi:10.1109/tfuzz.2004.834818

Cited by 14 publications

(11 citation statements)

References 14 publications

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“…The algorithm has been demonstrated to build a robot controller capable of performing obstacle avoidance in a realworld environment (Thongchai 2002) and the algorithm has been shown to cope effectively with limited noise (Carmona et al 2004). Fully understanding the implications of noise is important for a control problem with a continuous state space.…”

Section: Wang and Mendelmentioning

confidence: 99%

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Learning a robot controller using an adaptive hierarchical fuzzy rule-based system

Waldock

Carse

2015

Soft Comput

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The majority of machine learning techniques applied to learning a robot controller generalise over either a uniform or pre-defined representation that is selected by a human designer. The approach taken in this paper is to reduce the reliance on the human designer by adapting the representation to improve the generalisation during the learning process. An extension of a Hierarchical Fuzzy Rule-Based System (HFRBS) is proposed that identifies and refines inaccurate regions of a fuzzy controller, while interacting with the environment, for both supervised and reinforcement learning problems. The paper shows that a controller using an adaptive HFRBS can learn a suitable control policy using a fewer number of fuzzy rules for both a supervised and reinforcement learning problem and is not sensitive to the layout as with a uniform representation. In supervised learning problems, a small number of extra trials are required to find an effective representation but for reinforcement learning problems, the process of adapting the representation is shown to significantly reduce the time taken to learn a suitable control policy and hence open the door to high-dimensional problems.

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Section: Wang and Mendelmentioning

confidence: 99%

“…When this count extends a threshold, the fuzzy rule uses the most activated consequent. In this paper, an extended version (Carmona et al 2004) of a method proposed by Ishibuchi et al (1992) and Nozaki et al (1997) is used to learn the consequent of a fuzzy rule using the relative mean degree of certainty.…”

Section: Ishibuchimentioning

confidence: 99%

Learning a robot controller using an adaptive hierarchical fuzzy rule-based system

Waldock

Carse

2015

Soft Comput

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“…For example, methods such as the Wang and Mendel algorithm [33] or the input space oriented strategy [34] are biased by covering criteria trying to ensure a high covering degree that sometimes is not needed. The mixed method (MM) [35] consists of adding rules to the linguistic model obtained by the Wang and Mendel algorithm in the fuzzy input subspaces that have examples but do not yet have a rule, trying to improve the accuracy of the linguistic model by adding even more rules. However, these kinds of methods are still useful to obtain a set of promising candidate rules in order to subsequently select those with the best cooperation as a second stage [36], [37].…”

Section: Interaction Between the Rule Selection And The Lateral Tmentioning

confidence: 99%

A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection

Alcalá

Alcalá‐Fdez

Herrera

2007

IEEE Trans. Fuzzy Syst.

170

149

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Abstract-Linguistic fuzzy modeling allows us to deal with the modeling of systems by building a linguistic model which is clearly interpretable by human beings. However, since the accuracy and the interpretability of the obtained model are contradictory properties, the necessity of improving the accuracy of the linguistic model arises when complex systems are modeled. To solve this problem, one of the research lines in recent years has led to the objective of giving more accuracy to linguistic fuzzy modeling without losing the interpretability to a high level.In this paper, a new postprocessing approach is proposed to perform an evolutionary lateral tuning of membership functions, with the main aim of obtaining linguistic models with higher levels of accuracy while maintaining good interpretability.To do so, we consider a new rule representation scheme base on the linguistic 2-tuples representation model which allows the lateral variation of the involved labels. Furthermore, the cooperation of the lateral tuning together with fuzzy rule reduction mechanisms is studied in this paper, presenting results on different real applications. The obtained results show the good performance of the proposed approach in high-dimensional problems and its ability to cooperate with methods to remove unnecessary rules.

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“…4 Scheme of the behavior of the crossover operators based on environments the Wang and Mendel algorithm [42] or the Input Space oriented Strategy [28] are biased by covering criteria trying to ensure a high covering degree that sometimes is not needed. The Mixed Method (MM) [6] consists of adding rules to the linguistic model obtained by the Wang and Mendel algorithm in the fuzzy input subspaces that having examples do not still have a rule, trying to improve the linguistic model accuracy by adding even more rules. However, these kinds of methods are still useful to obtain a set of promising candidate rules in order to subsequently select those with the best cooperation as a second stage [11,29].…”

Section: Rule Selection and La-tuningmentioning

confidence: 99%

Rule Base Reduction and Genetic Tuning of Fuzzy Systems Based on the Linguistic 3-tuples Representation

et al. 2006

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Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation model, that allows the symbolic translation of a label considering an unique parameter. It involves a reduction of the search space that eases the derivation of optimal models. This work presents a new symbolic representation with three values (s, α, β), respectively representing a label, the lateral displacement and the amplitude variation of the support of this label. Based on this new representation we propose a new method for fine tuning of membership functions that is combined with a rule base reduction method in order to extract the most useful tuned rules. This approach makes use of a modified inference system that consider non-covered inputs in order to improve the final fuzzy model generalization ability, specially in highly non-linear problems with noise points. Additionally, we analyze the proposed approach showing its behavior in two real-world applications.

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Strategies to Identify Fuzzy Rules Directly From Certainty Degrees: A Comparison and a Proposal

Cited by 14 publications

References 14 publications

Learning a robot controller using an adaptive hierarchical fuzzy rule-based system

Learning a robot controller using an adaptive hierarchical fuzzy rule-based system

A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection

Rule Base Reduction and Genetic Tuning of Fuzzy Systems Based on the Linguistic 3-tuples Representation

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