An Agent-Based Fuzzy Collaborative Intelligence Approach for Precise and Accurate Semiconductor Yield Forecasting

Chen, Toly; Wang, Yi‐Chi

doi:10.1109/tfuzz.2013.2250290

Cited by 40 publications

(7 citation statements)

References 43 publications

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“…Zarandi et al [13] proposed a four-layer fuzzy multiagent system to forecast next-day stock prices based on collaboration among software agents. Chen and Wang [28] and Chen and Romanowski [29] proposed software agents, rather than real experts, for fuzzy collaborative forecasting to expedite collaboration. However, software agents usually follow pre-specified rules when fuzzy parameters need to be adjusted, which may result in unrealistic fuzzy forecasts.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

2020

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Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.

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Section: Literature Reviewmentioning

confidence: 99%

“…If o is large, it becomes difficult to optimize NLP Model I. Therefore, Chen and Wang [28] advised choosing o ∈ [0, 4]. When o is a positive integer, the model can be converted into an equivalent QP problem.…”

Section: Preliminary Models For Fitting a Fuzzy Linear Regressionmentioning

confidence: 99%

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

2020

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“…They proposed three aggregation rules: The minimum aggregation rule, the maximum aggregation rule, and the average aggregation rule, for which the degree of consensus was measured in terms of the highest discrepancy. FI is the most prevalent consensus aggregator in FCF methods [24,29]. FI finds out the values common to those estimated by all DMs.…”

Section: Fi For Finding Out the Overall Consensusmentioning

confidence: 99%

“…Most existing FCF approaches adopt a posterior aggregation, i.e., the forecasts by multiple DMs, rather than their opinions, are aggregated [21,22]. If the forecasting performance based on the aggregation result is not satisfactory, the DMs modify their opinions by referring to others' opinions [23,24]. However, existing FCF methods belong to supervised learning methods, while the PCPA-FAHP approach does not because there is no actual value of the fuzzy weight.…”

Section: Introductionmentioning

confidence: 99%

A Partial-Consensus Posterior-Aggregation FAHP Method—Supplier Selection Problem as an Example

Wang

Chen

2019

Mathematics

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Existing fuzzy analytic hierarchy process (FAHP) methods usually aggregate the fuzzy pairwise comparison results produced by multiple decision-makers (DMs) rather than the fuzzy weights estimations. This is problematic because fuzzy pairwise comparison results are subject to uncertainty and lack consensus. To address this problem, a partial-consensus posterior-aggregation FAHP (PCPA-FAHP) approach is proposed in this study. The PCPA-FAHP approach seeks a partial consensus among most DMs instead of an overall consensus among all DMs, thereby increasing the possibility of reaching a consensus. Subsequently, the aggregation result is defuzzified using the prevalent center-of-gravity method. The PCPA-FAHP approach was applied to a supplier selection problem to validate its effectiveness. According to the experimental results, the PCPA-FAHP approach not only successfully found out the partial consensus among the DMs, but also shrunk the widths of the estimated fuzzy weights to enhance the precision of the FAHP analysis.

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“…It has been successfully applied to a great variety of different processes such as control engineering, signal processing, information processing, machine intelligence, decision making, management, finance, medicine, and robotics [1–9]. …”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy System Based on Rectangular Pyramid

Jiang

Yuan

et al. 2015

The Scientific World Journal

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A new fuzzy system is proposed in this paper. The novelty of the proposed system is mainly in the compound of the antecedents, which is based on the proposed rectangular pyramid membership function instead of t-norm. It is proved that the system is capable of approximating any continuous function of two variables to arbitrary degree on a compact domain. Moreover, this paper provides one sufficient condition of approximating function so that the new fuzzy system can approximate any continuous function of two variables with bounded partial derivatives. Finally, simulation examples are given to show how the proposed fuzzy system can be effectively used for function approximation.

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An Agent-Based Fuzzy Collaborative Intelligence Approach for Precise and Accurate Semiconductor Yield Forecasting

Cited by 40 publications

References 43 publications

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

A Partial-Consensus Posterior-Aggregation FAHP Method—Supplier Selection Problem as an Example

A New Fuzzy System Based on Rectangular Pyramid

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