scholarly journals Fuzzy interpolation by convex completion of sparse rule bases

2002 ◽  
Author(s):  
L. Ughetto ◽  
D. Dubois ◽  
H. Prade
Keyword(s):  
Fuzzy Interpolation ◽  
Rule Bases

scholarly journals Improved Method for Predicting the Performance of the Physical Links in Telecommunications Access Networks

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ferenc Lilik ◽  
Szilvia Nagy ◽  
László T. Kóczy
Keyword(s):  
Wavelet Transformation ◽  
Fuzzy Inference ◽  
Data Transfer ◽  
Continuous Functions ◽  
Access Networks ◽  
Electrical Parameters ◽  
Inference System ◽  
Fuzzy Interpolation ◽  
Novel Approach ◽  
Rule Bases

A novel approach is presented which is able to predict the available maximal data transfer rate of SHDSL connections from measured frequency dependent electrical parameters of wire pairs. Predictions are made by a fuzzy inference system. The basis of the operable and tested method will be introduced, then an improved version is shown, in which the problems derived from sampling of continuous functions of electrical parameters are eliminated by wavelet transformation. Also possibilities for simplification of the problem and a way of reducing the dimensions of the applied rule bases are presented. As the set of the measured data leads to sparse rule bases, handling of sparseness is unavoidable. Two different ways—fuzzy interpolation and various membership functions—will be introduced. The presented methods were tested by measurements in real telecommunications access networks.

An Alternative Backward Fuzzy Rule Interpolation Method

2014 ◽  
Vol 6 (4) ◽  
pp. 47-71 ◽  
Author(s):  
Shangzhu Jin
Keyword(s):  
Analogical Reasoning ◽  
Interpolation Method ◽  
Effective Means ◽  
Fuzzy Rule ◽  
Final Conclusion ◽  
Rule Based ◽  
Fuzzy Interpolation ◽  
Fuzzy Models ◽  
Real World Applications ◽  
Rule Bases

Fuzzy set theory allows for the inclusion of vague human assessments in computing problems. Also, it provides an effective means for conflict resolution of multiple criteria and better assessment of options. Fuzzy rule interpolation offers a useful means for enhancing the robustness of fuzzy models by making inference possible in sparse rule-based systems. However, in real-world applications of inter-connected rule bases, situations may arise when certain crucial antecedents are absent from given observations. If such missing antecedents were involved in the subsequent interpolation process, the final conclusion would not be deducible using conventional means. To address this issue, an approach named backward fuzzy rule interpolation and extrapolation has been proposed recently, allowing the observations which directly relate to the conclusion to be inferred or interpolated from the known antecedents and conclusion. As such, it significantly extends the existing fuzzy rule interpolation techniques. However, the current idea has only been implemented via the use of the scale and move transformation-based fuzzy interpolation method, which utilise analogical reasoning mechanisms. In order to strengthen the versatility and feasibility of backward fuzzy interpolative reasoning, in this paper, an alternative a-cut-based interpolation method is proposed. Two numerical examples and comparative studies are provided in order to demonstrate the efficacy of the proposed work.

scholarly journals Fuzzy Interpolation Systems and Applications

10.5772/68051 ◽  
2017 ◽  
Author(s):  
Longzhi Yang ◽  
Zheming Zuo ◽  
Fei Chao ◽  
Yanpeng Qu
Keyword(s):  
Fuzzy Interpolation ◽  
Interpolation Systems

scholarly journals Approximate reasoning with fuzzy rule interpolation: background and recent advances

2021 ◽  
Author(s):  
Fangyi Li ◽  
Changjing Shang ◽  
Ying Li ◽  
Jing Yang ◽  
Qiang Shen
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Approximate Reasoning ◽  
Rule Based ◽  
Reasoning Systems ◽  
Significant Interest ◽  
Recent Developments ◽  
Rule Bases ◽  
The Given ◽  
Computing Approach

AbstractApproximate 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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