• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.334-340
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• 2002

This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

• Computers and Concrete
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• v.21 no.1
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• pp.103-110
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• 2018

This study introduces the supervised committee fuzzy model as a hybrid fuzzy model to predict compressive strength (CS) of geopolymers prepared from alumina-silica products. For this purpose, more than 50 experimental data that evaluated the effect of $Al_2O_3/SiO_2$, $Na_2O/Al_2O_3$, $Na_2O/H_2O$ and Na/[Na+K] on (CS) of geopolymers were collected from the literature. Then, three different Fuzzy Logic (FL) models (Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL)) were adopted to overcome the inherent uncertainty of geochemical parameters and to predict CS. After validating the model, it was found that the SFL model is superior to MFL and LFL models, but each of the FL models has advantages to predict CS. Therefore, to achieve the optimal performance, the supervised committee fuzzy logic (SCFL) model was developed as a hybrid method to combine the benefits of individual FL models. The SCFL employs an artificial neural network (ANN) model to re-predict the CS of three FL model predictions. The results also show significant fitting improvement in comparison with individual FL models.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.529-535
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• 2014

In this paper, we propose a hybrid approach towards multiple kernels interval type-2 possibilistic fuzzy C-means(PFCM) based on interval type-2 possibilistic fuzzy c-means(IT2PFCM) and possibilistic fuzzy c-means using multiple kernels( PFCM-MK). In case of noisy data or overlapping cluster prototypes, fuzzy C-means gives poor performance in comparison to possibilistic fuzzy C-means(PFCM). Moreover, to address the uncertainty associated with fuzzifier parameter m, interval type-2 possibilistic fuzzy C-means(PFCM) is used. Most of the practical data available are complex and non-linearly separable. In such cases using Gaussian kernels proves helpful. Therefore, in order to overcome all these issues, we have integrated multiple kernels possibilistic fuzzy C-means(PFCM) into interval type-2 possibilistic fuzzy C-means(IT2PFCM) and propose the idea of multiple kernels based interval type-2 possibilistic fuzzy C-means(IT2PFCM-MK).

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.3 no.2
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• pp.31-37
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• 2007

The fuzzy control algorithm for HVAC system has been developed for minimizing energy consumption while maintaining the comfort of indoor thermal environment in terms of the environmental variables such as time varying indoor cooling load and outdoor temperatures. The optimal set-points of control parameters with fuzzy control are supply air temperature, chilled water temperature and condenser temperature. This study has been done by using TRNSYS program in order to analyze the HVAC system response. As a result, the fuzzy control algorithm with PID algorithm shows good energy performance in comparison with conventional one.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.189-192
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• 1996

This paper presents a systematic approach to identify a linguistic fuzzy model for a multi-input and single-output complex system. Such a model is composed of fuzzy rules, and its output is inferred by the simplified reasoning. The structure and membership function parameters for a fuzzy model are automatically and simultaneously identified by GA (Genetic Algorithm). After GA search, optimal parameters for the fuzzy model are finely tuned by a gradient method. A numerical example is provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce the linguistic fuzzy model with higher accuracy and a smaller number of rules than the ones achieved previously in other methods.

• The Journal of Information Systems
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• v.19 no.3
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• pp.59-73
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• 2010

Along with advance of information technology, value of information is growing much more than ever. And nearly all organizations pay great attentions to information security to protect their own important informations against every kind of hazardous accidents. Therefore, organizations want to select best information security system among many possible alternatives. For this purpose, several fuzzy AHP decision making approaches can be utilized. In this study, we consider a number of qualitative and quantitative factors to evaluate security systems and then apply three fuzzy AHP approaches for simple case to compare the results from three approaches. We find that final decision depends on both fuzzy AHP methods and degree of fuzziness.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.101-108
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• 2000

In this study, an expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The expert system developed in this study have two main parts named pre-module and post-module. Pre-module decides tunnel information imput items based on the tunnel face mapping information which can be easily obtained in-situ site. Then, using fuzzy quantification theory II, fuzzy membership function is composed and tunnel safety level is inferred through this membership function. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river, This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.249-252
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• 1996

Fuzzy logic has been successfully used for nonlinear control systems. However, when the plant is complex or expert knowledge is not available, it is difficult to construct the rule bases of fuzzy systems. In this paper, we propose a new method of how to construct automatically the rule bases using fuzzy neural network. Whereas the conventional methods need the training data representing input-output relationship, the proposed algorithm utilizes the gradient of the object function for the construction of fuzzy rules and the tuning of membership functions. Experimental results with the inverted pendulum show the superiority of the proposed method in comparison to the conventional fuzzy controller.

• ETRI Journal
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• v.37 no.3
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• pp.502-511
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• 2015

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.

• Journal of the Ergonomics Society of Korea
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• v.15 no.1
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• pp.15-26
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• 1996

When a product is designed, it is important to consider its image on consumers. In this study, we developed a technique to measure product image. Because human image of a product is very subjective and fuzzy, it is difficult to measure easily. To deal with this difficulty effectively, we used fuzzy- weighted checklist. The fuzzy-weighted checklist presents a fuzzy version of the weighted checklist technique for evaluating or comparing complex systems or subjects. In this technique, we used a pairwise comparison method to obtain the relative importance weights of image factors. Also, we used linguistic ratings to obtain the scores of image factors for a product. Then, we synthesized the scores of image factors to obtain a fuzzy composite score and its linguistic approximation. The entire procedure of this technique was written in quick Basic. As an example, this techinque is applied to car evaluation. The results show that this technique can be effectively used to the quqntitative evaluation of huamn image.