Modeling of hierarchical fuzzy systems

Lee, Ming-Ling; Chung, Habong; Yu, Fang‐Ming

doi:10.1016/s0165-0114(02)00517-1

Cited by 145 publications

(85 citation statements)

References 9 publications

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“…Supposing that there are m independent variables and each of these variables has v membership functions, then the number of rules equals to vm in NHFMs while there are [(m − 1)*v 2 ] rules in HFMs (6,7,32,33). Examining the HFM that has v fuzzy sets and m independent variables (Figure 2), it is seen that intermediate outputs (U 1 ,U 2 ,…,U m-2 ) and dependent variable Ŷ= U m-1 are calculated by adding independent variables (X 1 ,X 2 ,…,X m ) to the model hierarchically.…”

Section: Hierarchical Fuzzy Model Structurementioning

confidence: 99%

Comparison of Hierarchical and Non-hierarchical Fuzzy Models with Simulation and an Application on Hypertension Data Set

Cantaş¹,

Ömürlü²,

Türe³

2018

meandros

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Anah tar Ke li me ler Öz AbstractObjective: The aim of this study is to compare the classification performances of hierarchical and non-hierarchical fuzzy models built using different membership functions. Materials and Methods:In this study, normally distributed data sets containing different number of independent variables (p=3 and p=6) were generated. Besides, the classification performances of hierarchical and non-hierarchical fuzzy models built by using the data set which contained in body mass index, fasting blood glucose and triglyceride values of hypertensive (n=206) and control (n=113) people were compared. Results: It was found that there was a significant difference between the fuzzy models (p<0.001). According to the result of both simulation and hypertension data set application, non-hierarchical fuzzy models were found to have better classification performance than hierarchical fuzzy models according to sensitivity, specificity, accuracy and root mean square criteria. Moreover; when number of independent variables was increased, performances of the models increased too and approached to each other. Conclusion:In fuzzy logic methods; data structure, distributions of the variables and correlation between them, how to divide independent variables into categories and which of the fuzzy logic methods is to choose should be examined by taking an expert support.Amaç: Bu çalışmanın amacı farklı üyelik fonksiyonları ile oluşturulan aşamalı ve aşamalı olmayan bulanık modellerin sınıflandırma performanslarının karşılaştırılmasıdır. Gereç ve Yöntemler: Bu çalışmada farklı sayıda (p=3 ve p=6) bağımsız değişkenler içeren normal dağılıma uygunluk gösteren veri setleri türetildi. Ayrıca hipertansif (n=206) ve kontrol (n=113) bireylerine ilişkin beden kitle indeksi, açlık kan şekeri ve trigliserid değerlerini içeren veri seti kullanılarak oluşturulan aşamalı ve aşamalı olmayan bulanık modellerin sınıflandırma performansları karşılaştırıldı. Bulgular: Bulanık modeller arasında ileri düzeyde farklılık olduğu bulundu (p<0.001). Hem simülasyon hem de hipertansiyon veri seti sonuçlarına göre, aşamalı olmayan bulanık modellerin duyarlılık, özgüllük, doğruluk ve hata kareler ortalamasının

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Section: Hierarchical Fuzzy Model Structurementioning

confidence: 99%

Comparison of Hierarchical and Non-hierarchical Fuzzy Models with Simulation and an Application on Hypertension Data Set

Cantaş¹,

Ömürlü²,

Türe³

2018

meandros

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“…The hierarchical fuzzy systems (HFS) [19] [7] have the advantage that the total number of rules is greatly reduced by a hierarchical structure, linear with the number of input variables [12]. A HFS divides the inference into stages so that a subset of input variables produce intermediate results and these results are taken as inputs in subsequent stages whereas, the intermediate results may also possess interpretable meaning.…”

Section: Principlesmentioning

confidence: 99%

A Novel Approach for Performance Characterization of IaaS Clouds

Ács

Németh

Gergely

2014

Lecture Notes in Computer Science

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Abstract. Infrastructure-as-a-Service (IaaS) clouds are widely used today, however there are no standardized or commonly used performance evaluation methods and metrics that can be used to compare the services of the different providers. Performance evaluation tools and benchmarks are able to grasp some aspects or details of performance but for various reasons are not capable to characterize cloud performance. Our aim is to collect these elementary or primitive facets of performance and derive high-level aggregated and qualitative performance characterization semantically far above the output of tools and benchmarks. We designed and implemented a framework that collects low-level raw performance data (in terms of CPU, disk, memory and network) of cloud providers based on standard benchmark tools and these data are aggregated and evaluated using a hierarchical fuzzy system. In this process performance characteristics are associated with symbolic values and fuzzy inference is applied to produce the normalized qualitative comparable and readable performance metrics. In this paper, we discuss the issues of cloud performance analysis, present the concept and implementation of our method, illustrate the proposed solution by comparing -in terms of performancethe general purpose medium instance type of the Amazon EC2 cloud (in Ireland) and the standard instance type of the OpenNebula installation at MTA SZTAKI.

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“…On the basis of the experience with neural networks, it is advisable to deal with complex systems by decomposing them by choosing an overarching structure of various fuzzy inference systems (FISs) before data mining (Geman et al 1992). For modelling real-world problems, hierarchical FLMs have three advantages: interpretability, accuracy, and dimensionality reduction (Lee et al 2003;Liu and Li 2005;Zeng and Keane 2005).…”

Section: Fuzzy Logic Modellingmentioning

confidence: 99%

“…Methods of rule definition based on computer learning generally produce meaningless intermediate variables (Lee et al 2003). We implemented the output of the FRFs in the third layer only, which is identical to the solution presented by Lee et al (2003) to reduce rules in HFS. This procedure contributed Fig.…”

Section: On Manual Calibration Of Hierarchical Fuzzy Systemsmentioning

confidence: 99%

Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam

et al. 2010

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To reveal farmers' motives for on-farm diversification and integration of farming components in the Mekong Delta, Vietnam, we developed a fuzzy logic model (FLM) using a 10-step approach. Farmers' decision-making was mimicked in a three-layer hierarchical architecture of fuzzy inference systems, using data of 72 farms. The model includes three variables for family motives of diversification, six variables related to component integration, next to variables for the production factors and for farmers' appreciation of market prices and know-how on 10 components. To obtain a good classification rate of the less frequent activities, additional individual fine-tuning was necessary after general model calibration. To obtain the desired degree of sensitivity to each variable, it was necessary to use up to five linguistic values for some of the input and output variables in the intermediate hierarchical layers. Model's sensitivity to motivational variables determining diversification and integration was of the same magnitude as its sensitivity to market prices and farmers' know-how of the activities, but less than its sensitivity to labour, capital and land endowment. Modelling to support strategic decision-making seems too elaborate for individual farms, but FLM will be useful to integrate farmers' opinions in strategic decision-making at higher hierarchical levels.

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Modeling of hierarchical fuzzy systems

Cited by 145 publications

References 9 publications

Comparison of Hierarchical and Non-hierarchical Fuzzy Models with Simulation and an Application on Hypertension Data Set

Comparison of Hierarchical and Non-hierarchical Fuzzy Models with Simulation and an Application on Hypertension Data Set

A Novel Approach for Performance Characterization of IaaS Clouds

Using fuzzy logic modelling to simulate farmers’ decision-making on diversification and integration in the Mekong Delta, Vietnam

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