• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.494-498
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• 1996

Three fuzzy input space partitoining methods, which are grid, tree, and scatter method, are mainly used until now. These partition methods represent good performance in the modeling of the linear system and nonlinear system with independent modeling variables. But in the case of the nonlinear system with the coupled modeling variables, there should be many fuzzy rules for acquiring the exact fuzzy model. In this paper, it shows that the fuzzy model is acquired using transformed modeling vector by linear transformation of the modeling vector.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.23-30
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• 2004

In order to approximate the spatial query result size we partition the input rectangles into subsets and estimate the query result size based on the partitioned spatial area. In this paper we examine query result size estimation in skewed data. We examine the existing spatial partitioning techniques such as equi-area and equi-count partitioning, which are analogous to the equi-width and equi-height histograms used in relational databases, and examine the other partitioning techniques based on spatial indexing. In this paper we propose a new spatial partitioning technique based on the Hilbert space filling curve. We present a detailed experimental evaluation comparing the proposed technique and the existing techniques using synthetic as well as real-life datasets. The experiments showed that the proposed partitioning technique based on the Hilbert space filling curve achieves better query result size estimation than the existing techniques for space query size, bucket numbers, skewed data, and spatial data size.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.493-496
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• 2000

In this paper, an adaptive neuro-fuzzy controller using the conditional fuzzy c-means(CFCM) methods is proposed. Usually, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Finally, we applied the proposed method to the water path temperature control system and obtained a better performance than previous works.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.531-540
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• 2008

An Adaptive Random Testing(ART) is one of test case generation algorithms, which was designed to get better performance in terms of fault-detection capability than that of Random Testing(RT) algorithm by locating test cases in evenly spreaded area. Two ART algorithms, such as Distance-based ART(D-ART) and Restricted Random Testing(RRT), had been indicated that they have significant drawbacks in computations, i.e., consuming quadratic order of runtime. To reduce the amount of computations of D-ART and RRT, iterative partitioning of input domain strategy was proposed. They achieved, to some extent, the moderate computation cost with relatively high performance of fault detection. Those algorithms, however, have yet the patterns of non-uniform distribution in test cases, which obstructs the scalability. In this paper we analyze the distribution of test cases in an iterative partitioning strategy, and propose a new method of input domain enlargement which makes the test cases get much evenly distributed. The simulation results show that the proposed one has about 3 percent of improvement in terms of mean relative F-measure for 2-dimension input domain, and shows 10 percent improvement for 3-dimension space.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.274-283
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• 2002

This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.451-461
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• 2009

The Adaptive Random Testing(ART) aims to improve the performance of traditional Random Testing(RT) by reducing the number of test cases to find the failure region which is located in the input domain. Such enhancement can be obtained by efficient selection algorithms of test cases. The ART through Iterative Partitioning(IP-ART) is one of ART techniques and it uses an iterative input domain partitioning method to improve the performance of early-versions of ART which have significant drawbacks in computation time. And the IP-ART with Enlarged Input Domain(EIP-ART), an improved version of IP-ART, is known to make additional performance improvement with scalability by expanding to virtual test space beyond real input domain of IP-ART. The EIP-ART algorithm, however, have the drawback of heavy cost of computation time to generate test cases mainly due to the virtual input domain enlargement. For this reason, two algorithms are proposed in this paper to mitigate the computation overhead of the EIP-ART. In the experiments by simulations, the tiling technique of input domain, one of two proposed algorithms, showed significant improvements in terms of computation time and testing performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.5
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• pp.550-557
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• 1999

This paper describes a road-following controller using the proposed neural network for autonomous vehicle. Road-following with visual sensor like camera requires intelligent control algorithm because analysis of relation from road image to steering control is complex. The proposed neural network, relative similarity modular network(RSMN), is composed of some learning networks and a partitioniing network. The partitioning network divides input space into multiple sections by similarity of input data. Because divided section has simlar input patterns, RSMN can learn nonlinear relation such as road-following with visual control easily. Visual control uses two criteria on road image from camera; one is position of vanishing point of road, the other is slope of vanishing line of road. The controller using neural network has input of two criteria and output of steering angle. To confirm performance of the proposed neural network controller, a software is developed to simulate vehicle dynamics, camera image generation, visual control, and road-following. Also, prototype autonomous electric vehicle is developed, and usefulness of the controller is verified by physical driving test.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.378-384
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• 2013

In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.