Group decision making under generalized fuzzy soft sets and limited cognition of decision makers

Chen, Weijie; Zou, Yan

doi:10.1016/j.engappai.2019.103344

Cited by 21 publications

(6 citation statements)

References 33 publications

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“…where shows union of overall admissible k and v [49]. The model considers the Takagi-Sugeno-Kang (TSK) fuzzy rule class, which has higher accuracy than the Mamdani rule [50]. The singleton fuzzifier was used in this study [51].…”

Section: B Type-2 Fuzzy Systemmentioning

confidence: 99%

U-Shaped Low-Complexity Type-2 Fuzzy LSTM Neural Network for Speech Enhancement

et al. 2023

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Speech enhancement (SE) aims to improve the intelligibility and perceptual quality of speech contaminated by noise signals through spectral or temporal changes. Deep learning models achieve speech enhancement and estimate the magnitude spectrum. This paper proposes a novel and computationally efficient deep learning model to enhance noisy speech. The model pre-processes the noisy speech magnitude by redistributing energy from high-energy voiced segments to low-energy unvoiced segments using an adaptive power law transformation while maintaining the total energy of the speech signals constant. A U-shaped fuzzy long short-term memory (UFLSTM) estimates the magnitude of a time-frequency (T-F) mask by using the pre-processed data. Residual connections to the similar-shaped layers are added to avoid gradient decay. Attention process is adopted by modifying the forget gate of UFLSTM. To make a causal speech enhancement system, the processing does not include any future audio frames. We compare the proposed speech enhancement to other deep learning models in different noisy environments with signal-to-noise ratios of 0 dB, 5 dB, and 10 dB. The experiments show that the proposed SE system outscores the competing deep learning models and considerably improves speech intelligibility and quality. In terms of STOI and PESQ, the LibriSpeech database improves results by (0.211) 21.1% and (0.95) 36.39%, respectively, over noisy speech in seen noisy conditions, and by (0.199) 19.9% and (0.94) 35.69% over noisy speech in unseen noisy conditions. Further, the cross-corpus analysis shows that proposed SE system performs better when trained with the DNS dataset as compared to the LibriSpeech, VoiceBank, and TIMIT datasets.

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Section: B Type-2 Fuzzy Systemmentioning

confidence: 99%

U-Shaped Low-Complexity Type-2 Fuzzy LSTM Neural Network for Speech Enhancement

et al. 2023

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“…Firstly, the fuzzy sets [21][22] A1, A2 and A3 are taken to represent three levels of user tag weights, namely "small, moderate and large" respectively, and the corresponding ones are generated MDF [23], as shown in Figure 4 (taking social tag as an example). In this paper, gaussian function [24] is used to represent fuzzy sets.…”

Section: Determination Of Membership Degree Function (Mdf)mentioning

confidence: 99%

User tag weight assessment based on fuzzy theory in mobile social networks

Gao

Wang

et al. 2021

Preprint

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Mobile social network supports mobile communication and asynchronous social networking. For enterprises, how to provide better services and create greater business value through the data and information provided by users is crucial. For example, enterprises need to build user profiles to achieve personalized recommendation and precision marketing. In view of the data modeling stage of user profile, we propose a method to evaluate user tag weight, which includes two steps. Specifically, we introduce fuzzy theory to get the initial weight interval. Then, genetic algorithm with single point crossover is used to optimize user tag weight. Experiment results show that the proposed method has better performance than other three methods applied to recommendation system.

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“…Under this method, other fuzzy decision‐making algorithm which did not use HFLPR to present decision makers’ opinions in the situation could be adopted to our system. For example, Chen and Zou 34 develop a fuzzy GDM method based on the generalized fuzzy soft set. The new input form which could enable the user to input decision makers’ decision fuzzy soft set can be customized by our original input form, that is, different from HFLPR which need to provide the maximum and minimum LTs for each element, the user only need to provide a single value for decision maker's evaluation (single value) of an alternative under a criterion; therefore the input form will be changed to simplified.…”

Section: Data Translation Workflowmentioning

confidence: 99%