• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.1-102
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• 2002

In this paper, we propose Self-organizing neurofuzzy networks(SONFN) and discuss their comprehensive design methodology. The proposed SONFN is generated from the mutually combined structure of both neurofuzzy networks (NFN) and polynomial neural networks(PNN) for model identification of complex and nonlinear systems. NFN contributes to the formation of the premise part of the SONFN. The consequence part of the SONFN is designed using PNN. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. We discuss two kinds of SONFN architectures and propose a comprehensive learning algorithm. It is shown that this network...

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.1
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• pp.36-41
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• 2009

Self organizing map(SOM) is one of the unsupervised methods based on the competitive learning. Many clustering works have been performed using SOM. It has offered the data visualization according to its result. The visualized result has been used for decision process of descriptive data mining as exploratory data analysis. In this paper we propose improvement of SOM using stratified sampling of statistics. The stratification leads to improve the performance of SOM. To verify improvement of our study, we make comparative experiments using the data sets form UCI machine learning repository and simulation data.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2451-2460
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• 2000

In order to minimize straightness error of deflected shaft, a geometric adaptive straightness controller system is studied. A multi-step straightening and a three-point bending process have been developed for the geometric adaptive straightness controller. Load-deflection relationship, on-line identification of variations of material properties, on-line springback prediction, and real-time hydraulic control methodology are studied for the three-point bending process. By deflection pattern analysis and fuzzy self-learning method in the multi-step straightening process, a straightening point and direction, desired permanent deflection and supporting condition are determined. An automatic straightening machine has been fabricated for rack bars by using the developed ideas. Validity of the proposed system is verified through experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.608-611
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• 2003

Performance assessment was introduced to improvement of self-directed learning and method of assessment for differenced learning as the seventh educational curriculum is enforced. Performance assessment is overcoming limitation about problem solving ability and higher thinking abilities assessment that is problem of a written examination and get into the spotlight by way for quality of class and school normalization. But performance assessment has problems about possibilities of assessment fault by appraisal, fairness, reliability, and validity of grading, ambiguity of grading standard, difficulty about objectivity security etc. This study proposes fuzzy performance assessment system to solve problem of the conventional performance assessment. This paper presented an objective and reliable performance assessment method through fuzzy reasoning, design fuzzy membership function and define fuzzy rule analyzing factor that influence in each sacred ground of performance assessment to account principle subject. Also, performance assessment item divides by formation estimation and subject estimation and designed membership function in proposed performance assessment method. Performance assessment result that is worked through fuzzy performance assessment system can pare down burden about appraisal's fault and provide fair and reliable assessment result through grading that have correct standard and consistency to students.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.372-372
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• 2000

In this paper, we propose Neuro Fuzzy Polynomial Networks(NFPN) based on Polynomial Neural Network(PNN) and Neuro-Fuzzy(NF) for model identification of complex and nonlinear systems. The proposed NFPN is generated from the mutually combined structure of both NF and PNN. The one and the other are considered as the premise part and consequence part of NFPN structure respectively. As the premise part of NFPN, NF uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. As the consequence part of NFPN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. NFPN is available effectively for multi-input variables and high-order polynomial according to the combination of NF with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. In order to evaluate the performance of proposed models, we use the nonlinear function. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.883-889
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• 2005

Error backpropagation algorithm of multilayer perceptron may result in local-minima because of the insufficient nodes in the hidden layer, inadequate momentum set-up, and initial weights. In this paper, we proposed the ART-1 based fuzzy supervised learning algorithm which is composed of ART-1 and fuzzy single layer supervised learning algorithm. The Proposed fuzzy supervised learning algorithm using self-generation method applied not only ART-1 to creation of nodes from the input layer to the hidden layer, but also the winer-take-all method, modifying stored patterns according to specific patterns. to adjustment of weights. We have applied the proposed learning method to the problem of recognizing a resident registration number in resident cards. Our experimental result showed that the possibility of local-minima was decreased and the teaming speed and the paralysis were improved more than the conventional error backpropagation algorithm.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.1
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• pp.1-11
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• 1994

This paper presents an intelligent robotic control method for chamferless parts mating by integrating fuzzy control and neural network. The successful assembly task requires an extremely high position accuracy and a good knowledge of mating parts. However, conventional assembly method alone makes it difficult to achieve satisfactory assembly performance because of the complexity and the uncertainties of the process and its environments such as not only the limitation of the devices performing the assembly but also imperfect knowledge of the parts being assembled. To cope with these problems, an intelligent robotic assembly method is proposed, which is composed of fuzzy controller and learning mechanism based upon neural net. In this method, fuzzy controller copes with the complexity and the uncertainties of the assembly process, while neural network enhances the assembly scheme so as to learn fuzzy rules from experience and adapt to changes in environment of uncertainty and imprecision. The performance of the proposed assembly scheme is evaluted through a series of experiments using SCARA robot. The results show that the proposed control method can be effectively applied to chamferless precision parts mating.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.688-694
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• 2005

In this Paper, we Propose a new neuro-fuzzy modeling using clustering-based learning method. In the proposed clustering method, number of clusters is automatically inferred and its parameters are optimized simultaneously, Also, a neuro-fuzzy model is learned based on clustering information at same time. In the previous modelling method, clustering and model learning are performed independently and have no exchange of its informations. However, in the proposed method, overall neuro-fuzzy model is generated by using both clustering and model learning, and the information of modelling output is used to clustering of input. The proposed method improve the computational load of modeling using Subtractive clustering method. Simulation results show that the proposed method has an effectiveness compared with the previous methods.

• International Journal of Automotive Technology
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• v.4 no.4
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• pp.181-191
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• 2003

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, this paper proposes a new control strategy for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. In the control scheme, the PID controller is used to minimize vehicle body vertical acceleration, the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to improve the adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, the gain values and the membership functions of fuzzy logic controller on-line. Taking a four degree-of-freedom nonlinear vehicle model as example, the proposed control scheme is applied and the simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and in attenuating the excessive dynamic tire load to enhance road holding performance. The stability and adaptability are also showed even when the system is subject to severe road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and the active vehicle suspension systems by using, e.g., linear fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability can improve vehicle ride comfort performance significantly and offer better system robustness.