Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction

Wang, Lixin

doi:10.1109/tfuzz.2019.2930488

Cited by 37 publications

(30 citation statements)

References 56 publications

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“…At the same time, the actual application requires a large amount of data verification and analysis. Therefore, the future studies will further integrate big data [44] and artificial intelligence to deal with complex intelligent decision-making problems with unknown weights [45].…”

Section: Discussionmentioning

confidence: 99%

“…(3) Calculate expert weight matrix 𝜆 of different alternatives. First, the positive and negative ideal distances are calculated by formula (45) and (46) (5) Calculate the attribute weight of the matrix R. Use the CRITIC method described in section 5.3 to calculate the attribute weights of the aggregation matrix R. Using formula (58) to calculate the attribute weight, the results are as follows:…”

Section: Casementioning

confidence: 99%

“…formula (43) and (44), then positive and negative ideal distance are calculated by formulas (45) and ( 46)…”

Section: Declarationsmentioning

confidence: 99%

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An Integrated Group Decision-making Method for Hypertension Risk Management Under Interval-valued q-rung Othopair Fuzzy

Wan

Zhou

Liang

et al. 2022

Preprint

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This paper develops a new group decision-making (GDM) method for multi-attribute group decision-making (MAGDM) problems with unknown weights, which integrates Yager operator, CRITIC and WASPAS decision methods under interval-valued q-rung orthopair fuzzy sets (IVq-ROFS). The main contributions of this paper are concluded as below: (1) We first extend the distance measurement to interval-valued q-rung fuzzy numbers (IVq-ROFNs), and proves its properties. And we develop a new score function which is used to measure IVq-ROFNs, and the new score function overcomes the deficiencies of the existing score function. (2) We extend Yager weighted average operator (IVq-ROFYWA) and Yager weighted geometric average operator (IVq-ROFYWG) under IVq-ROFS, and we discuss the properties of IVq-ROFYWA and IVq-ROFYWG operators. (3) We propose a new method to derive expert weights of different alternatives, which can avoid the deficiency of overall decision-making perspective. And we extend deriving attribute weights method based on CRITIC under IVq-ROFS. (4) A new integrated MAGDM method is proposed. The MAGDM method integrates Yager operator, score function, distance measurement, expert weights deriving method, CRITIC method, and WASPAS method. (5) Finally, the group decision-making algorithm is used for hypertension risk management. The results are consistent with experts’ opinions. And comparing and analyzing results show that the proposed MAGDM method is effective and feasible.

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Section: Discussionmentioning

confidence: 99%

Section: Casementioning

confidence: 99%

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An Integrated Group Decision-making Method for Hypertension Risk Management Under Interval-valued q-rung Othopair Fuzzy

Wan

Zhou

Liang

et al. 2022

Preprint

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“…Likewise, to make a worldwide financial prediction, Lee ( 2020 ) introduced a chaotic type-2 transient-fuzzy deep neuro-oscillatory network (CT2TFDNN) with retrograde signaling. Other studies (Chandrasekar 2020 ; Chen et al 2020a , b ; Wang 2020 ; Xiao 2020 ) have implemented the DNFS method in Bitcoin price prediction, stock index prediction, and e-commerce platforms.…”

Section: Analysis and Synthesis Of Datamentioning

confidence: 99%

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey

et al. 2022

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Deep neural networks (DNN) have remarkably progressed in applications involving large and complex datasets but have been criticized as a black-box. This downside has recently become a motivation for the research community to pursue the ideas of hybrid approaches, resulting in novel hybrid systems classified as deep neuro-fuzzy systems (DNFS). Studies regarding the implementation of DNFS have rapidly increased in the domains of computing, healthcare, transportation, and finance with high interpretability and reasonable accuracy. However, relatively few survey studies have been found in the literature to provide a comprehensive insight into this domain. Therefore, this study aims to perform a systematic review to evaluate the current progress, trends, arising issues, research gaps, challenges, and future scope related to DNFS studies. A study mapping process was prepared to guide a systematic search for publications related to DNFS published between 2015 and 2020 using five established scientific directories. As a result, a total of 105 studies were identified and critically analyzed to address research questions with the objectives: (i) to understand the concept of DNFS; (ii) to find out DNFS optimization methods; (iii) to visualize the intensity of work carried out in DNFS domain; and (iv) to highlight DNFS application subjects and domains. We believe that this study provides up-to-date guidance for future research in the DNFS domain, allowing for more effective advancement in techniques and processes. The analysis made in this review proves that DNFS-based research is actively growing with a substantial implementation and application scope in the future.

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“…Sezer and Ozbayoglu [126] Gudelek et al [38] also apply CNN to stock price movement forecast using ETFs. Wang [151] build a deep convolutional fuzzy systems (DCFS) and fast training algorithms for the DCFS for the forecast of Hang Seng Index (HSI) of the Hong Kong stock market.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Stock Market Analysis with Text Data: A Review

Fataliyev,

Chivukula,

Prasad

et al. 2021

Preprint

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Stock market movements are influenced by public and private information shared through news articles, company reports, and social media discussions. Analyzing these vast sources of data can give market participants an edge to make profit. However, the majority of the studies in the literature are based on traditional approaches that come short in analyzing unstructured, vast textual data. In this study, we provide a review on the immense amount of existing literature of text-based stock market analysis. We present input data types and cover main textual data sources and variations. Feature representation techniques are then presented. Then, we cover the analysis techniques and create a taxonomy of the main stock market forecast models. Importantly, we discuss representative work in each category of the taxonomy, analyzing their respective contributions. Finally, this paper shows the findings on unaddressed open problems and gives suggestions for future work. The aim of this study is to survey the main stock market analysis models, text representation techniques for financial market prediction, shortcomings of existing techniques, and propose promising directions for future research.

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Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction

Cited by 37 publications

References 56 publications

An Integrated Group Decision-making Method for Hypertension Risk Management Under Interval-valued q-rung Othopair Fuzzy

An Integrated Group Decision-making Method for Hypertension Risk Management Under Interval-valued q-rung Othopair Fuzzy

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey

Stock Market Analysis with Text Data: A Review

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