Double-Input Rule Modules Stacked Deep Interval Type-2 Fuzzy Model with Application to Time Series Forecasting

Peng, Wei; Zhou, Changgeng; Li, Chengdong; Deng, Xiaoping; Zhang, Guiqing

doi:10.1007/s40815-021-01087-w

Cited by 13 publications

(5 citation statements)

References 39 publications

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“…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]. The Karnik-Mendel (K.M.)…”

Section: B Type-2 Fuzzy Systemmentioning

confidence: 99%

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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%

“…We have performed an experimental study to examine the cross-corpus generalization of neural models. The datasets including DNS [51], TIMIT [68], LibriSpeech [41], and VoiceBank [69] are examined for the speech quality and intelligibility. A speech dataset usually composed of different utterances uttered by various speakers.…”

Section: H Cross Corpus Analysismentioning

confidence: 99%

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

et al. 2023

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“…You can see that a data-driven approach changes sharp box uncertainty to flexible box uncertainty. This method reduces the sharpness and makes it softer and smoother [ 32 ]. Therefore, it decreases conservative and risk-averse.…”

Section: Problem Statementmentioning

confidence: 99%

Hybrid Fuzzy and Data-Driven Robust Optimization for Resilience and Sustainable Health Care Supply Chain with Vendor-Managed Inventory Approach

Lotfi

Kargar

Rajabzadeh

et al. 2022

Int. J. Fuzzy Syst.

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One of the problems that government managers deal with are medical inventory management in COVID-19 conditions. Based on this situation, the best strategy for managing and reducing inventory costs can be Vendor-Managed Inventory (VMI) policy in the recent decade. Therefore, a hybrid fuzzy and data-driven robust optimization for Resilience and Sustainable Health Care Supply Chain (RSHCSC) with VMI approach is appropriate for improving the inventory management system and tackling uncertainty and disruption in this situation. Three RSHCSC models are suggested using hybrid fuzzy and data-driven robust optimization with a stochastic programming approach. The first model is average and mean absolute function, the second model is Conditional Value at Risk (CVaR), the third model is Minimax model, and the final model is the traditional inventory model. Each of the proposed models has advantages and disadvantages that depend on the conservative level of decision-maker. Sensitivity analysis is done on essential parameters like fuzzy cut, confidence level, robust and resilience coefficient, and size models. The results show that increasing fuzzy cut, confidence level, robustification coefficient, resiliency coefficient, and CVaR confidence level amount of costs grows. The Minimax function is suitable for conservative decision-makers.

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“…The TSK fuzzy rule type was considered in the proposed model, which has more precision than Mamdani rules [49]. In this work, the singleton fuzzifier was implemented [50,51]. For defuzzification in the last step, the Karnik-Mendel (K.M.)…”

Section: A Review Of Interval Type-2 Fuzzy Setsmentioning

confidence: 99%

“…The cause of the pandemic was later recognized as a novel coronavirus known as SAR-COV-2. As of November 2020, there have been 51,100,521 COVID-19 cases, with 1,324,714 deaths and 32,951,517 recovered cases. The U.S. had the highest number of COVID-19 death cases with 247,000 deaths [1], see Table 1.…”

Section: Introductionmentioning

confidence: 99%

A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction

Safari

Hosseini

Mazinani

2021

Journal of Biomedical Informatics

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Currently, the novel COVID-19 coronavirus has been widely spread as a global pandemic. The COVID-19 pandemic has a major influence on human life, healthcare systems, and the economy. There are a large number of methods available for predicting the incidence of the virus. A complex and non-stationary problem such as the COVID-19 pandemic is characterized by high levels of uncertainty in its behavior during the pandemic time. The fuzzy logic, especially Type-2 Fuzzy Logic, is a robust and capable model to cope with high-order uncertainties associated with non-stationary time-dependent features. The main objective of the current study is to present a novel Deep Interval Type-2 Fuzzy LSTM (DIT2FLSTM) model for prediction of the COVID-19 incidence, including new cases, recovery cases, and mortality rate in both short and long time series. The proposed model was evaluated on real datasets produced by the world health organization (WHO) on top highly risked countries, including the USA, Brazil, Russia, India, Peru, Spain, Italy, Iran, Germany, and the U.K. The results confirm the superiority of the DIT2FLSTM model with an average area under the ROC curve (AUC) of 96% and a 95% confidence interval of [92-97] % in the short-term and long-term. The DIT2FLSTM was applied to a well-known standard benchmark, the Mackey-Glass time-series, to show the robustness and proficiency of the proposed model in uncertain and chaotic time series problems. The results were evaluated using a 10-fold cross-validation technique and statistically validated through the t-test method. The proposed DIT2FLSTM model is promising for the prediction of complex problems such as the COVID-19 pandemic and making strategic prevention decisions to save more lives.

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Double-Input Rule Modules Stacked Deep Interval Type-2 Fuzzy Model with Application to Time Series Forecasting

Cited by 13 publications

References 39 publications

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

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

Hybrid Fuzzy and Data-Driven Robust Optimization for Resilience and Sustainable Health Care Supply Chain with Vendor-Managed Inventory Approach

A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction

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