Hierarchical fuzzy control

Raju, G.V.S.; Jun, Zhou; Kisner, R.A.

doi:10.1080/00207179108934205

Cited by 369 publications

(193 citation statements)

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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%

“…In NHFM approach; as the number of independent variables increases, the number of rules that are used to make decision about dependent variable increases exponentially in knowledge base, which causes "curse of dimensionality" due to the fact that the number of adaptive parameters increases so much especially when there are too many independent variables (5). In order to overcome this problem, HFMs are suggested since the number of rules are linearly increases (5)(6)(7)(8). The aim of this study is comparing the classification performances of HFMs and NHFMs using different membership functions.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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“…First, the system's fuzzy rules increase exponentially with the input variables; second, the system's parameters to be designed will also increase exponentially with the input variables. To tackle this difficulty, Raju proposed the term Hierarchical Fuzzy Systems (HFS) [11]. HFSs are very useful for overcoming the curse of dimensionality, and make the rule base easier to understand and interpret.…”

Section: Introductionmentioning

confidence: 99%

Design of a hierarchical fuzzy model predictive controller

Fallah

Khanesar

Teshnehlab

2015

IJET

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In order to control a nonlinear system using Nonlinear Model Predictive Control (NMPC), a nonlinear model from system is required. In this paper, a hierarchical neuro-fuzzy model is used for nonlinear identification of the plant. The use of hierarchical neuro-fuzzy systems makes it possible to overcome the curse of dimensionality. In neuro-fuzzy systems, if the input number increases, then the number of rules increases exponentially. One solution to this problem is making use of Hierarchical Fuzzy System Mamdani (HFS) in which the number of the rules increases linearly. Gradient descent and recursive least square algorithm are used simultaneously to train the parameters of the HFS. Gradient Descent Algorithm is utilized to train the parameters, which appear nonlinearly in the output of HFS, and RLS is used to train the parameters of consequent the part, which appears linearly in the output of HFS. Finally, a model predictive fuzzy controller based on a predictive cost function is proposed. Using Gradient Descent Algorithm, the parameters of the controller are optimized. The proposed controller is simulated on the control of continuous stirred tank reactor. It is shown that the proposed method can control the system with high performance.

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Hierarchical fuzzy control

Cited by 369 publications

References 4 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

Design of a hierarchical fuzzy model predictive controller

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