Broad fuzzy cognitive map systems for time series classification

Wu, Kai; Yuan, Kaixin; Teng, Yingzhi; Liu, Jing; Jiao, Licheng

doi:10.1016/j.asoc.2022.109458

Cited by 12 publications

(3 citation statements)

References 53 publications

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“…The FCM's structure must also be reevaluated using fuzzy conditional statements or fuzzy rules in order to obtain the corresponding numerical values in the aforementioned range. Various algorithms might be utilized for this meta-learning process [22,23]. The most well-known, however, is the one by [18,24,25], which is as follows:…”

Section: Fcm Development Using Expert Knowledgementioning

confidence: 99%

“…An expert group of professionals in the field of urban mobility, along with various tightly interested stakeholders representing associations, agencies, companies, and research institutions, was assembled. According to [22,26,27], the number of specialists may vary depending on the specific renewable energy transition occurring at the time. However, according to studies on how fuzzy cognitive mapping (FCM) learning is structured, a minimum of seven experts are needed [28][29][30][31].…”

Section: Development Of a Dss For The Transition To Co 2 -Minimized U...mentioning

confidence: 99%

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A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

Kokkinos

Nathanail

2023

Sustainability

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The CO2 reduction promise must be widely adopted if governments are to decrease future emissions and alter the trajectory of urban mobility. However, from a long-term perspective, the strategic vision of CO2 mitigation is driven by inherent uncertainty and unanticipated volatility. As these issues emerge, they have a considerable impact on the future trends produced by a number of exogenous and endogenous factors, including Political, Economic, Social, Technological, Environmental, and Legal aspects (PESTEL). This study’s goal is to identify, categorize, and analyze major PESTEL factors that have an impact on the dynamics of urban mobility in a rapidly changing environment. For the example scenario of the city of Larissa, Greece, a Fuzzy Cognitive Map (FCM) approach was employed to examine the dynamic interactions and behaviors of the connected criteria from the previous PESTEL categories. An integrative strategy that evaluates the interaction of linguistic evaluations in the FCM is used to include all stakeholders in the creation of a Decision Support System (DSS). The methodology eliminates the uncertainty brought on by a dearth of quantitative data. The scenarios in the study strands highlight how urbanization’s effects on sustainable urban transportation and the emergence of urban PESTEL actors impact on CO2 reduction decision-making. We focus on the use case of Larissa, Greece (the city of the CIVITAS program), which began putting its sustainable urban development plan into practice in 2015. The proposed decision-making tool uses analytics and optimization algorithms to point responsible authorities and decision-makers in the direction of Larissa’s sustainable urban mobility and eventually the decarbonization of the urban and suburban regions.

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Section: Fcm Development Using Expert Knowledgementioning

confidence: 99%

Section: Development Of a Dss For The Transition To Co 2 -Minimized U...mentioning

confidence: 99%

A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

Kokkinos

Nathanail

2023

Sustainability

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“…Pei et al [22] presented a hidden-unit logistic model (HULM) to construct the latent structure within the data. Well-known feature-based algorithms include the learned pattern similarity [23], bag of SFA symbols (BOSS) [24], time series forest [25], TS-CHIEF [26], temporal feature fusion [27], and fuzzy cognitive map [28].…”

mentioning

confidence: 99%

DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification

Xiao,

Xu,

Xing

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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This paper proposes a dual-network-based feature extractor, perceptive capsule network (PCapN), for multivariate time series classification (MTSC), including a local feature network (LFN) and a global relation network (GRN). The LFN has two heads (i.e., Head A and Head B), each containing two squash CNN blocks and one dynamic routing block to extract the local features from the data and mine the connections among them. The GRN consists of two capsule-based transformer blocks and one dynamic routing block to capture the global patterns of each variable and correlate the useful information of multiple variables. Unfortunately, it is difficult to directly deploy PCapN on mobile devices due to its strict requirement for computing resources. So, this paper designs a lightweight capsule network (LCapN) to mimic the cumbersome PCapN. To promote knowledge transfer from PCapN to LCapN, this paper proposes a deep transformer capsule mutual (DTCM) distillation method. It is targeted and offline, using one-and two-way operations to supervise the knowledge distillation process for the dualnetwork-based student and teacher models. Experimental results show that the proposed PCapN and DTCM achieve excellent performance on UEA2018 datasets regarding top-1 accuracy.

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Introduction to Fuzzy Cognitive Map-Based Classification

Jastrzębska,

Nápoles

2024

Fuzzy Cognitive Maps

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Broad fuzzy cognitive map systems for time series classification

Cited by 12 publications

References 53 publications

A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO2 Urban Mobility: The Case of Larissa, Greece

DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification

Introduction to Fuzzy Cognitive Map-Based Classification

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