• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.5
• /
• pp.512-519
• /
• 2003

In this paper, we propose a neuro-fuzzy modeling to improve the performance using the hierarchical clustering and Gaussian Mixture Model(GMM). The hierarchical clustering algorithm has a property of producing unique parameters for the given data because it does not use the object function to perform the clustering. After optimizing the obtained parameters using the GMM, we apply them as initial parameters for Adaptive Network-based Fuzzy Inference System. Here, the number of fuzzy rules becomes to the cluster numbers. From this, we can improve the performance index and reduce the number of rules simultaneously. The proposed method is verified by applying to a neuro-fuzzy modeling for Box-Jenkins s gas furnace data and Sugeno's nonlinear system, which yields better results than previous oiles.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.3
• /
• pp.183-188
• /
• 2005

Although it is widely used to find an optimum setting of manufacturing process parameters in a variety of engineering fields, the Taguchi method has a difficulty in dealing with multi-response situations in which several response variables should be considered at the same time. For example, electrode wear, surface roughness, and material removal rate are important process response variables in an electrical discharge machining (EDM) process. A simultaneous optimization should be accomplished. Many researches from various disciplines have been conducted for such multi-response optimizations. One of them is a fuzzy logic approach presented by Lin et al. [1]. They showed that two response characteristics are converted into a single performance index based upon fuzzy logic. However, it is pointed out that information regarding relative importance of response variables is not considered in that method. In order to overcome this problem, a desirability function can be adopted, which frequently appears in the statistical literature. In this paper, we propose a novel approach for the multi-response optimization by incorporating fuzzy logic into desirability function. The present method is illustrated by an EDM data of Lin and Lin [2].

• KIEE International Transactions on Power Engineering
• /
• v.3A no.1
• /
• pp.7-16
• /
• 2003

A new method to evaluate the protection capability of distribution systems is reported in this paper. This work describes the fuzzy evaluation attributes and aggregation method of evaluation results based on a hierarchical model and the modified combination rule. An evaluation grade index called "protectability" is proposed and is expected to be a very uscful tool in defining an optimal protection and realizing the adaptive protection.rotection.

• Proceedings of the KSRS Conference
• /
• 2004.10a
• /
• pp.111-114
• /
• 2004

Object-based segmentation is the first essential step for image processing applications. Recently, SAR (Synthetic Aperture Radar) segmentation techniques have been developed, however not enough to preserve the significant information contained in the small regions of the images. The proposed method is to partition an SAR image into homogeneous regions by using a fuzzy hit-or-miss operator with an inherent spatial transformation, which endows to preserve the small regions. In our algorithm, an iterative segmentation technique is formulated as a consequential process. Then, each time in iterating, hypothesis testing is used to evaluate the quality of the segmented regions with a homogeneity index. The segmentation algorithm is unsupervised and employed few parameters, most of which can be calculated from the input data. This comparative study indicates that the new iterative segmentation algorithm provides acceptable results as seen in the tested examples of satellite images.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.45
• /
• pp.225-235
• /
• 1998

The scientific evaluation on enterprise helps establishing better management policy. Financial index has been used for the enterprise evaluation as the objective data. However, the necessity of the subjective data for competitive power evaluation is advocated recently. Therefore, in this paper, we propose an evaluation model of competitive power on enterprise by fuzzy integral, using the objective and the subjective data. The evaluation factors are composed to the financial index, top management, product, organization and enterprise's environment. These factors are grouped by detailed sub-factors of 16 units. Lastly, utilizing these factors, the efficiency of this method was shown by the result of the case study of 10 manufacturing enterprises.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1102-1105
• /
• 1993

A fuzzy expert system for prediction and mitigation of sludge bulking was developed for an activated sludge process which treats waste water from a food industry. The developed system is able not only to infer the degree of progress of sludge bulking but also to generate remedial operation guides which may be sent to the local controllers as remote set points. One of the important consequences through this study is the BI (Bulking Index) inferred by the bulking prediction expert system was found to have a close correlation with the SVI (Sludge Volume Index) which is a practical measure of degree of bulking but needs tedious chores for its measurement.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.12a
• /
• pp.333-336
• /
• 2002

This paper presents a new image segmentation method using fuzzy measures which reflect the local property of an image as well as the global property of an image An image is globally segmented into the crisp region and the ambiguous region in terms of the Index of fuzziness measured over all pixels of an image. The ambiguous region is luther partitioned into background and object in terms of the index of fuzziness computed over the set of neighboring pixels reflecting the local property most. From the experimental results, this method shows the effective ambiguity handling capability in segmenting an image.

• Proceedings of the KIEE Conference
• /
• 1999.07g
• /
• pp.2911-2913
• /
• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• International journal of advanced smart convergence
• /
• v.9 no.4
• /
• pp.52-61
• /
• 2020

The AHP method was used in 30 provinces of China to construct the index system of creative space efficiency evaluation and determine the weight of each index. The fuzzy comprehensive evaluation method was further used to score the indexes at all levels, and then the total efficiency score was sorted. The purpose of this study is to adjust the regional layout of creative space reasonably and implement financial policies accurately through the evaluation of the efficiency of creative space. The results is ranking top in weight of several indicators, which include the number of incubated Startups, the number of innovation and entrepreneurship mentors, the survival rate of incubator, the innovative training activities, etc. It was also found that Beijing, Shanghai, Jiangsu, Guangdong and Zhejiang ranked first in the score of creative space efficiency. This study is meaningful in that it was In order to effectively solve the problem of the imbalance of the creative space efficiency in China's province, by coordinating the regional pattern, establishing a sound service system and improving the efficiency evaluation system.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.6
• /
• pp.1182-1189
• /
• 1994

In this paper, we evaluate the performance of a fuzzy controller with a self-learning structure. The fuzzy controller is based on a fuzzy logic that approximates and effectively represents the uncertain phenomena of the real world. The fuzzy controller has control of a plant with a fuzzy inference logic. However, it is not easy to decide the membership function of a fuzzy controller and its controlrule. This problem can be solved by designing a self-learning controller that improves its own contropllaw to its goal with a performance table. The fuzzy controller is implemented with a 386PC, an interface board, a D/A converter, a PWM(Pulse Width Modulation) motor drive-circuit, and a sensing circuit, for error and differential of error. Since a Ball and Beam System is used in the experiment, the validity of the fuzzy controller with the self-learning structure can be evaluated through the actual experiment and the computer simulation of the real plant. The self-learning fuzzy controller reduces settling time by just under 10%.