A fuzzy c-medians variant for the generation of fuzzy term sets

Liao, T. Warren

doi:10.1002/int.1001

Cited by 15 publications

(4 citation statements)

References 17 publications

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“…For each metric, two functions describing membership to the fuzzy subsets favorable ( F ) and unfavorable ( U ) were defined. As values in the fuzzy range are simultaneously F and U , two complementary S‐shaped quadratic functions (Liao, 2002) are used as transition probabilities in the range F to U (and vice versa). The full procedure is detailed in the paper of Bellocchi et al (2002).…”

Section: Methodsmentioning

confidence: 99%

An integrated procedure to evaluate hydrological models

Confalonieri

Bregaglio

Bocchi

et al. 2010

Hydrological Processes

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Abstract:The growing concern for health-related problems deriving from pollutants leaching is driving national and international administrations to support the development of tools for evaluating the effects of alternate management scenarios and identifying vulnerable areas. Cropping systems models are powerful tools for evaluating leachates under different environmental, social, and management conditions. As percolating water is the transport vehicle for pollutants transport in soil, a reliable evaluation of water balance models is a fundamental prerequisite for investigating pesticides and nitrate fate. As specific approaches for the evaluation of multi-layer evolution of state variables are missing, we propose a fuzzy-based, integrated indicator (I SWC : 0, best; 1, worst) for a comprehensive evaluation of soil water content (SWC) simulations. We aggregated error metrics with others quantifying the homogeneity of errors across different soil layers, the capability of models to reproduce complex dynamics function of both time and soil depth, and model complexity. We tested I SWC on a sample dataset where the models CropSyst and CERES-Wheat were used to simulate SWC for winter wheat systems. I SWC revealed that, in the explored conditions, the global assessment of the two models' performances allowed identification of CropSyst as the best (average I SWC D 0Ð441, with a value of 0Ð537 obtained by CERES-Wheat), although each model prevailed for some of the metrics. CropSyst presented the highest accuracy (average agreement module D 0Ð400), whereas CERES-Wheat's accuracy was slightly worse, although achieved with a simplified modelling approach (average Akaike Information Criterion D 230Ð44), thereby favouring largearea applicability. The non-univocal scores achieved by the models for the different metrics support the use of multi-metric evaluation approaches for quantifying the different aspects of water balance model performances.

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Section: Methodsmentioning

confidence: 99%

An integrated procedure to evaluate hydrological models

Confalonieri

Bregaglio

Bocchi

et al. 2010

Hydrological Processes

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“…However, the fuzzy c-means clustering algorithm has two major shortcomings: the insensitivity to the end classes and the irregular overlaps between classes. To overcome these problems, two modification steps were previously developed by Liao (20), summarized as follows:…”

Section: Fuzzification Of Driving States and Simple Time Lag Valuesmentioning

confidence: 99%

Variable Response Time Lag Module for Car-Following Models: Development and Structuring with Fuzzy Set Theory

Hatipkarasulu

Wolshon

2003

Transportation Research Record: Journal of the Transportation R

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The car-following process consists of a stimulus-response relationship between vehicles in which the driver of the following vehicle reacts to the actions of the lead vehicle after a time lag. Since the 1950s, the car-following phenomenon has been studied and analyzed, resulting in various models and algorithms. Throughout this period, driver response time lag has always been assumed to be a constant value for the driver at all times, regardless of the approach and level of detail of the model. The primary shortcoming of a constant time lag is that it introduces a number of strong assumptions that do not concur with human nature. To address the problems associated with constant time lag, an independent response time lag module was developed that can be used in any car-following model or algorithm without changing its fundamental mathematical structure. One of the most appealing aspects of this module is its flexible and transparent structure that can easily be adapted to and calibrated for any model or simulation algorithm. The development and structure of the module are described, including the fuzzy definitions of driving states, fuzzy rule extraction, and fuzzy time lag assignment. Statistical and graphical evaluations of the module performance are also included by integrating the module to a proportional car-following model. In the graphical evaluation, the module improved the model performance significantly by providing more precise timing for the driver response. Both Kolmogorov–Smirnov and root-mean-square error tests confirmed that the use of the module improves the car-following model performance.

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“…Several approaches can be used for the automatic generation of the KB from data, representing samples (or examples) of a problem. Clustering algorithms [18], neural networks [15], and Genetic Algorithms (GA) [7,13] are among the most well-succeeded approaches. Recently there has been a considerable research effort focusing on the use of GA in the design of fuzzy systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Feature Subset Selection Using the Wang & Mendel Method

Cintra¹,

Arruda²,

Monard

2008

2008 Eighth International Conference on Hybrid Intelligent Systems

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The dimension of a knowledge domain can impact the use of genetic algorithms to automatically design fuzzy rule bases, since the search space for the genetic algorithm increases exponentially with the number of features. Filters are a possible approach to reduce the number of features. However, the filter approach does not take into consideration the particular aspects of fuzzy logic when selecting or ranking features. This work presents a method for feature subset selection using the Wang & Mendel method as the base for a wrapper. Experimental results are presented and discussed.

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A fuzzy c-medians variant for the generation of fuzzy term sets

Cited by 15 publications

References 17 publications

An integrated procedure to evaluate hydrological models

An integrated procedure to evaluate hydrological models

Variable Response Time Lag Module for Car-Following Models: Development and Structuring with Fuzzy Set Theory

Fuzzy Feature Subset Selection Using the Wang & Mendel Method

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