Two-Stage Learning Based Fuzzy Cognitive Maps Reduction Approach

Hatwagner, Miklos F.; Yeşil, Engin; Dodurka, M. Furkan; Papageorgiou, Elpiniki I.; Urbas, Leon; Kóczy, László T.

doi:10.1109/tfuzz.2018.2793904

Cited by 22 publications

(9 citation statements)

References 20 publications

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“…• La interpretabilidad de los resultados (Gregor et al, 2017;Hatwagner & al., 2018): En particular, en la gestión de proyectos es imprescindible que los expertos humanos puedan interpretar fácilmente las decisiones por las herramientas propuestas por los investigadores.…”

Section: Los Mapas Cognitivos Difusos Ofrecen Ventajas Comounclassified

La crisis económica ante la pandemia COVID-19 en el Ecuador, periodo 2021.

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Uvidia

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2021

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El presente trabajo analiza el escenario económico de Ecuador en el contexto de la pandemia generada por el COVID-19. La pandemia está causando una gran conmoción dentro del estado ecuatoriano, el gran déficit económico que el Ecuador hoy en día está pasando es algo que nos pone al pueblo ecuatoriano en la mira de caer en una terrible crisis. El presente artículo tiene el propósito de dar a conocer sobre la crisis económica ante el COVID-19 en el Ecuador. La necesidad de conocer este tema es probablemente por el impacto que ha tenido dentro del desempleo, salud y educación y también el impacto corruptivo vive ante funcionarios antiéticos.

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Section: Los Mapas Cognitivos Difusos Ofrecen Ventajas Comounclassified

La crisis económica ante la pandemia COVID-19 en el Ecuador, periodo 2021.

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Uvidia

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2021

dilemas

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“…In this work, we use a hyperbolic tangent like many previous works [50], [68]- [70], which means that we need to watch for the presence of a chaotic attractor. Second, machine learning can be used to create [58], or improve [71] an FCM (e.g., ensuring that it converges faster without significantly changing the results [72]). As we take a participatory modeling approach (section II.B) rather than a data-driven approach, we build the FCM with experts rather than optimizing the weights based on data.…”

Section: How Can We Measure the Consequences Of An Action?mentioning

confidence: 99%

Identifying the Components and Interrelationships of Smart Cities in Indonesia: Supporting Policymaking via Fuzzy Cognitive Systems

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Multiple Indonesian cities currently aim to qualify as ''smart cities.'' Previous research on defining smart cities (e.g., the implementation-oriented maturity model) tends to focus on components over interrelationships, is challenging to apply to a specific context such as Indonesia, and offers limited support for policy-relevant questions. In this paper, we propose to address these shortcomings to support policymakers in identifying concrete action plans in Indonesia specifically. Our approach clarifies interrelationships for the context of use and supports structural (e.g., what aspects of a ''smart city'' are impacted by an intervention?) as well as what-if policy questions. We started with a systems' science approach to developing a cognitive map of the components and their interrelationships, as is increasingly done in participatory modeling and particularly in socio-ecological management. We transformed semi-structured interviews of 10 Indonesian experts into maps and assembled them to create the first comprehensive smart cities cognitive map for Indonesia, totaling 52 concepts and 98 relationships. While a cognitive map already provides support for decision-making (e.g., by identifying loops in the system), it is only conceptual and thus cannot form predictions. Consequently, we extended our cognitive map into a fuzzy cognitive map (FCM), whose inference abilities allow to examine the dynamic response of an indicator (e.g., ''smart city'') in response to different interventions. As fuzzy cognitive maps include the strengths of interrelationships but not the notion of time, future research may refine our model using system dynamics. This refinement would support policymakers in investigating when to conduct and/or evaluate an intervention. INDEX TERMS Cognitive computing, participatory modeling, public policy, systems for smart cities. I. INTRODUCTION The population is increasing not only around the world but also in Indonesia [1], [2]. This is paralleled with a tendency for people to cluster in cities, which are already central to Indonesia and are due to play an even more important role. According to Parasati, about 59.35% of people live in urban areas [3]. The share of the Indonesian population living in The associate editor coordinating the review of this manuscript and approving it for publication was Anna Visvizi. cities is predicted to reach 67.66% by 2025, and 82% by 2045. These dynamics have many consequences on economical and societal factors in cities [4]. In particular, the challenges that cities face will become even more pressing, calling for new infrastructures [5]. Challenges include waste management, traffic, and quality of life [6], [7]. Additional challenges come from changes in the age pyramid as the population gets older. Cities thus need new strategies [8], calling for innovative scenarios and solutions [9]. The concept of 'smart city' is one of the solutions.

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“…2 In FCMs based on the opinions of experts, it has been common for experts to express opinions using type-1 (T1) fuzzy sets (FSs) and rules. 19,20 For distinction, hereafter in the article we call such FCMs, T1-FCMs. Another important point revealed by the literature review is the different types of FCM extensions, which have been proposed for improving and strengthening the basic FCM in terms of its reasoning, predicting, learning, and modeling abilities.…”

Section: Introductionmentioning

confidence: 99%

“…The nature of a problem determines the type and the number of FCM nodes; which are often specified by experts. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] As for control problems, one may use the experiences of control engineers in designing FCMs. 28 However, this approach might not be efficient since one might face the following difficulties (1) not be able to find experts for each control applications, (2) the complexity of the resulting FCM might be too high and thus might not feasible for real-time applications, and (3) the stability of the resulting FCM cannot be shown which is an essential requirement in any control system.…”

Section: Introductionmentioning

confidence: 99%