Advances in Fuzzy Clustering and its Applications

Oliveira, José Valente de; Pedrycz, Witold

doi:10.1002/9780470061190

Cited by 249 publications

(11 citation statements)

References 23 publications

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“…Commonly, the optimal number of clusters can be selected so that it results in the clustering performance corresponding to the smallest Xie-Beni index. For details of FCMC and selecting cluster number, readers are guided to the previous works of Xie and Beni (1991), and of Oliveira and Pedrycz (2007).…”

Section: Estimation Of Eac Prediction Intervalmentioning

confidence: 99%

Interval Estimation of Construction Cost at Completion Using Least Squares Support Vector Machine

Cheng

Hoang

2014

Journal of Civil Engineering and Management

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Completing a project within the planned budget is the bottom-line of construction companies. To achieve this goal, periodic cost estimation is vitally important not only in the planning phase, but also in the execution phase. Due to high uncertainty in operational environment, point estimation of project cost is oftentimes not sufficient to assist the decision-making process. This study utilizes Least Squares Support Vector Machine (LS-SVM), machine learning based interval estimation (MLIE), and Differential Evolution (DE) to establish a novel model for predicting construction project cost. LS-SVM is a supervised learning technique used for regression analysis. MLIE is employed for inference of prediction intervals. Moreover, our model deploys DE in the cross validation process to search for the optimal values of tuning parameters. The newly developed model, named as EAC-LSPIM, yields results consisting of a point estimate coupled with lower and upper prediction limits, at a certain level of confidence, to accentuate uncertainty. Simulation and performance comparison demonstrate that the new model is capable of delivering accurate and reliable forecasting results.

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Section: Estimation Of Eac Prediction Intervalmentioning

confidence: 99%

Interval Estimation of Construction Cost at Completion Using Least Squares Support Vector Machine

Cheng

Hoang

2014

Journal of Civil Engineering and Management

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“…Clustering such as many other learning algorithms needs some train and test data. Each input element of clustering is a feature vector that is represented as Equation (2) (Valente & Pedrycz, 2007).…”

Section: Fuzzy C-mean Clustering Algorithmmentioning

confidence: 99%

A New Design of Intelligent Traffic Signal Control

Daneshfar

RavanJamjah

2013

International Journal of Fuzzy System Applications

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Dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This paper proposed an adaptive and cooperative multi-agentfuzzy system for a decentralized traffic signal control. The proposed model has three levels of control, the current intersection traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the current intersection traffic pattern. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. Also every intersection flow is predicted in two different ways: 1- through a recursive algorithm. 2- based on a two stage fuzzy clustering algorithm. The proposed solution is tested with traffic control of a large connected junction and the result obtained is promising in comparison to the conventional fixed sequence traffic signal and to the vehicle actuated traffic signal control strategies which are the most applicable strategies in this area. Also to simulate the proposed traffic control solutions, a Netlogo-based traffic simulator has been developed as the agents’ world which simulates the roads, traffic flow and intersections.

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“…В [1,2] развивается кластерно-вариационный метод (КВМ) решения задачи триангуляции (являющейся альтернативным по отношению к известным методам пассивной локации, например [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]), позволяющий при некотором ограничении на число недостоверных азимутальных и угломестных измерений формировать устойчивые оценки местоположения цели в условиях существенной априорной неопределенности (таких как отсутствие достоверных знаний о законах распределения ошибок измерений, пропуски в измерениях, наличие инструментальных и методических ошибок, «деградация» структуры системы и др.). В таких условиях, которые зачастую возникают на практике, результаты пеленгования могут допускать наличие неизвестных аномальных ошибок измерений (АОИ).…”

Section: Introductionunclassified

Оптимизация Кластерно-Вариационного Метода Построения Многопозиционной Пеленгационной Системы Для Условий Априорной Неопределенности

2023

Автомат. и телемех.

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Advances in Fuzzy Clustering and its Applications

Cited by 249 publications

References 23 publications

Interval Estimation of Construction Cost at Completion Using Least Squares Support Vector Machine

Interval Estimation of Construction Cost at Completion Using Least Squares Support Vector Machine

A New Design of Intelligent Traffic Signal Control

Оптимизация Кластерно-Вариационного Метода Построения Многопозиционной Пеленгационной Системы Для Условий Априорной Неопределенности

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