Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox

Johanyák, Zsolt Csaba; Tikk, Domonkos; Kovács, Szilveszter; Wong, Kok Wai

doi:10.1109/fuzzy.2006.1681736

Cited by 71 publications

(39 citation statements)

References 13 publications

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“…An extensive and detailed comparison with a larger set of examples and methods coded in the FRI toolbox [18], is under development. We believe that its conclusion will not reveal any major differences with the conclusions of the presented paper.…”

Section: Discussionmentioning

confidence: 99%

Double-linear fuzzy interpolation method

Detyniecki

Marsala

Rifqi

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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Abstract-In this paper, we present an original fuzzy interpolation method. In contrast to existing approaches, our method is able to always construct an interpolated fuzzy interval without a need of a special step dedicated to the "standardization" of nonviable solutions, which fractures the sense of the interpolation. In fact, these "standardization" steps imply that, for instance, a point obtained from the interpolation of the upper limit (right side) of the fuzzy sets, is used to build the lower limit (left side) of the interpolated conclusion, breaking the underlying hypothesis of (linear) graduality. To achieve the direct interpolation, our method is based on the deviation of the observation from the expected linearly interpolated solution and constrains of the constructed solution between extreme cases. We illustrate and discuss the behavior of our method by comparison to other wellknown fuzzy interpolation methods.

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Section: Discussionmentioning

confidence: 99%

Double-linear fuzzy interpolation method

Detyniecki

Marsala

Rifqi

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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“…4 and 5 show antecedent partitions of the two fuzzy rule bases and observations, in thick lines. Experiments were conducted using our MATLAB FRI Toolbox [26]. Settings are given on the toolbox home page (http://fri.gamf.hu/examples/).…”

Section: Methodsmentioning

confidence: 99%

“…These properties may serve as a standard and be used to guide FRIT classification and comparison. To facilitate comparison and evaluation, we recently created the MATLAB FRI Toolbox [26], which implements several important FRITs and can be extended by other contributors. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines

Tikk

Johanyák

Kovács

et al. 2011

J. Adv. Comput. Intell. Intell. Inform.

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This paper comprehensively analyzes Fuzzy Rule Interpolation and extrapolation Techniques (FRITs). Because extrapolation techniques are usually extensions of fuzzy rule interpolation, we treat them both as approximation techniques designed to be applied where sparse or incomplete fuzzy rule bases are used, i.e., when classical inference fails. FRITs have been investigated in the literature from aspects such as applicability to control problems, usefulness regarding complexity reduction and logic. Our objectives are to create an overall FRIT standard with a general set of criteria and to set a framework for guiding their classification and comparison. This paper is our initial investigation of FRITs. We plan to analyze details in later papers on how individual techniques satisfy the groups of criteria we propose. For analysis, MATLAB FRI Toolbox provides an easy-to-use testbed, as shown in experiments.

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“…In ordinal fuzzy controller [2], if the rule base is sparse (not complete), there could be an observation, which does not find any fuzzy rule to fire. In this case, Fuzzy Rule Interpolation (FRI) is necessary to be used [3][4][5]. When using FRI, it is not necessary to cover the entire universe of discourse of the antecedent parts of the fuzzy rules.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Automated Guided Vehicle Controller with Reverse Strategy

Kato

Wong²

2011

J. Adv. Comput. Intell. Intell. Inform.

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This paper describes the intelligent Automated Guided Vehicle (AGV) control system using Fuzzy Rule Interpolation (FRI) method. The AGV used in this paper is a virtual vehicle simulated using computer. The purpose of the control system is to control the simulated AGV by moving along the given path towards a goal. Some obstacles can be placed on or near the path to increase the difficulties of the control system. The intelligent AGV should follow the path by avoiding these obstacles. This system consists of two fuzzy controllers. One is the original FRI controller that mainly controls the forward movement of the AGV. Another one is the proposed reverse movement controller that deals with the critical situation. When the original FRI controller faces the critical situation, our proposed reverse controller will control the AGV to reverse and move forward towards the goal. Our proposed reverse controller utilizes the advantage of FRI method. In our system, we also develop a novel switching system to switch from original to the developed reverse controller.

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Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox

Cited by 71 publications

References 13 publications

Double-linear fuzzy interpolation method

Double-linear fuzzy interpolation method

Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines

Intelligent Automated Guided Vehicle Controller with Reverse Strategy

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