Backward Fuzzy Rule Interpolation

Jin, Shangzhu; Diao, Ren; Quek, Chai; Shen, Qiang

doi:10.1007/978-981-13-1654-8

Cited by 21 publications

(12 citation statements)

References 51 publications

(56 reference statements)

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“…As such, FRI methods may also be organised in two groups, respectively termed as non-transformation based and transformation based FRI (Chen and Adam 2018). The seminal work for fuzzy interpolative reasoning, as of the techniques reported in Kóczy and Hirota (1993a, b) and their extensions, form the most typical non-transformation based FRI. For those relying on transforming intermediate rules, a family of scale and move transformation-based FRI (termed as T-FRI), such as those given in Shen (2006, 2008), Jin et al (2014), Yang et al (2017) and Naik et al (2017b), have been popularly studied and widely applied despite their relative recency. This survey will review the general FRI methodologies as its first main topic.…”

Section: Research Contextmentioning

confidence: 99%

Approximate reasoning with fuzzy rule interpolation: background and recent advances

Shang

Liu

et al. 2021

Artif Intell Rev

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Approximate reasoning systems facilitate fuzzy inference through activating fuzzy if–then rules in which attribute values are imprecisely described. Fuzzy rule interpolation (FRI) supports such reasoning with sparse rule bases where certain observations may not match any existing fuzzy rules, through manipulation of rules that bear similarity with an unmatched observation. This differs from classical rule-based inference that requires direct pattern matching between observations and the given rules. FRI techniques have been continuously investigated for decades, resulting in various types of approach. Traditionally, it is typically assumed that all antecedent attributes in the rules are of equal significance in deriving the consequents. Recent studies have shown significant interest in developing enhanced FRI mechanisms where the rule antecedent attributes are associated with relative weights, signifying their different importance levels in influencing the generation of the conclusion, thereby improving the interpolation performance. This survey presents a systematic review of both traditional and recently developed FRI methodologies, categorised accordingly into two major groups: FRI with non-weighted rules and FRI with weighted rules. It introduces, and analyses, a range of commonly used representatives chosen from each of the two categories, offering a comprehensive tutorial for this important soft computing approach to rule-based inference. A comparative analysis of different FRI techniques is provided both within each category and between the two, highlighting the main strengths and limitations while applying such FRI mechanisms to different problems. Furthermore, commonly adopted criteria for FRI algorithm evaluation are outlined, and recent developments on weighted FRI methods are presented in a unified pseudo-code form, easing their understanding and facilitating their comparisons.

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Section: Research Contextmentioning

confidence: 99%

Approximate reasoning with fuzzy rule interpolation: background and recent advances

Shang

Liu

et al. 2021

Artif Intell Rev

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“…The above constraints are introduced as with those imposed over the weighting vector in the original T-FRI method, upon which this work aims to improve. This is a common practice followed by other related work (e.g., [18], [19], [21], [23]). Further to helping assess directly how much contribution each selected fuzzy rule may make to the overall formulation of the resulting intermediate rule, such constraints also make the subsequent computation simpler in deriving the weighting vector.…”

Section: Fuzzy Rule Interpolation With Selected Fuzzy Rulesmentioning

confidence: 99%

“…It then imposes the similarity measure computed between the observation and the intermediate rule antecedent over the consequent deduced by firing the intermediate rule. A particular and popular example of this category is the scale and move transformation-based fuzzy rule interpolation (TFRI) [16], which has led to a number of advanced theoretical developments and applications [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [11], [30], [31], [32]. A key concept used in TFRI is the representative value of a fuzzy set, which captures important geometric characteristics of the fuzzy set (e.g., shape and location).…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Transformation-Based Fuzzy Rule Interpolation

Chen

Shang

Yang

et al. 2020

IEEE Trans. Fuzzy Syst.

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Fuzzy rule interpolation (FRI) is of particular significance for reasoning in the presence of insufficient knowledge or sparse rule bases. As one of the most popular FRI methods, transformation-based fuzzy rule interpolation (TFRI) works by constructing an intermediate fuzzy rule, followed by running scale and move transformations. The process of intermediate rule construction selects a user-defined number of rules closest to an observation that does not match any existing rule, using a distance metric. It relies upon heuristically computed weights to assess the contribution of individual selected rules. This process requires a move operation in an effort to force the intermediate rule to overlap with an unmatched observation, regardless of what rules are selected and how much contribution they may each make. It is therefore, desirable to avoid this problem and also, to improve the automation of rule interpolation without resorting to the user's intervention for fixing the number of the closest rules. This paper proposes such a novel approach to selecting a subset of rules from the sparse rule base with an embedded rule weighting scheme for the automatic assembling of the intermediate rule. Systematic comparative experimental results are provided on a range of benchmark data sets, to demonstrate statistically significant improvement in the performance achieved by the proposed approach over that obtainable using conventional TFRI.

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“…FRI approaches have been further developed from different perspectives. For instance, adaptive fuzzy interpolation was proposed to guarantee the interpolated results are consistent throughout the inference processes [17], [18], [19], [20], [21]; backward fuzzy interpolation was proposed to support backward inference and allow flexible interpolation when certain antecedents are missing from the observation [22]; and roughfuzzy rule interpolation was proposed for both representing the knowledge involving higher order uncertainty and facilitating rule interpolation with such knowledge [23]. FRI approaches have also been extended to support Type-2 fuzzy sets, such as [24].…”

Section: A Fuzzy Inference and Interpolationmentioning

confidence: 99%

Manual Task Completion Time Estimation for Job Shop Scheduling Using a Fuzzy Inference System

Yang

Hackney

et al. 2017

2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and I

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Manual collating and packing is still the most cost-effective way of dispatching goods in many applications, despite of the rapid development of assembly robots. One such application, is the manufacturers of Point of Sale (POS) and Point of Purchase (POP) in the design and print industry; they produce and dispatch display objects in various quantities, shapes and sizes. The display objects, typically posters and 3D displays, are designed for different commercial promotion events in supermarkets, shopping malls and other high street shops. It is difficult to assemble and pack the objects using assembly robots due to the potential complexity and infinite variety of the tasks. The collate and pack department must manually pick, collate, assemble and pack items, often carried out in multiple lines based on the nature of the jobs, as the last stage of the manufacturing process. The jobs themselves are often unique bespoke arrangements defying a generic solution, flat-packed to minimise portage costs. The design of the lines and the schedule of the lines are determined by the area manager based on their expertise and historic knowledge, which seriously limits the effectiveness of the widely available automatic global scheduling system for these POP and POS print manufacturers. This paper proposes a job completion time estimation system which estimates the completion times for different tasks under different conditions such that the intelligent scheduling system can make a schedule globally by artificially treating the assembly lines as virtual machines. The system is implemented using a particular fuzzy inference system, fuzzy interpolation, and an illustrative example demonstrates the working and potential of the proposed solution.

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Backward Fuzzy Rule Interpolation

Cited by 21 publications

References 51 publications

Approximate reasoning with fuzzy rule interpolation: background and recent advances

Approximate reasoning with fuzzy rule interpolation: background and recent advances

A New Approach for Transformation-Based Fuzzy Rule Interpolation

Manual Task Completion Time Estimation for Job Shop Scheduling Using a Fuzzy Inference System

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