• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1841-1848
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• 1999

DELVAUX is a genetics-based inductive learning system that learns a rule-set, which consists of Bayesian classification rules, from sets of examples for classification tasks. One problem that DELVAUX faces in the rule-set learning process is that, occasionally, the learning process ends with a local optimum without finding the best rule-set. Another problem is that, occasionally, the learning process ends with a rule-set that performs well for the training examples but not for the unknown testing examples. This paper describes efforts to alleviate these two problems centering on the N-version learning approach, in which multiple rule-sets are learning and a classification system is constructed with those learned rule-sets to improve the overall performance of a classification system. For the implementation of the N-version learning approach, we propose a decision-making scheme that can draw a decision using multiple rule-sets and a genetic algorithm approach to find a good combination of rule-sets from a set of learned rule-sets. We also present empirical results that evaluate the effect of the N-version learning approach in the DELVAUX learning environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.303-308
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• 2006

To solve the rule explosion problem in multi input fuzzy logic system, a method of converting a given fuzzy system to 2 layered hierarchical fuzzy system is presented where the collection of the THEN-parts of the fuzzy rules of given fuzzy system is considered as vectors of fuzzy rule. At the 1 st layer, linearly independent fuzzy rule vectors generated from the given fuzzy logic system are used and, at the 2nd layer, linear combinations of these independent fuzzy rule vectors are used for fuzzy logic units at each layer. The resultant 2 layered hierarchical fuzzy system has not only equivalent approximation capability, but less number of fuzzy rules compared with the conventional fuzzy logic system.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.598-603
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• 2007

To solve the rule explosion problem in multi-input fuzzy system, a method of converting a given fuzzy system to 2 layered hierarchical fuzzy system has been reported, where at the 1st layer, linearly independent fuzzy rule vectors generated from the given fuzzy system are used and, at the 2nd layer, linear combinations of these independent fuzzy rule vectors are used. In this paper, the steapest descent algorithm is presented to learn the fuzzy rule vectors and related coefficients for the equivalent 2 layered hierarchical structure. By simulation of learning of ball and beam control system, the feasibility of proposed learning scheme is shown.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.37-43
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• 2011

In pattern analysis, dimensionality reduction and reasonable identification rule generation are very important parts. This paper performed effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, this paper constructed the hierarchically identifiable rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, this paper demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.279-287
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• 2007

Data fusion is the process of combining multiple data in order to produce information of tactical value to the user. Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. In this paper, we develop was macro program for statistical matching which is one of five branch types for data fusion. And then we apply data fusion and association rule techniques to environmental data.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1E
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• pp.3-8
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• 1997

Three new classification methods for multi temporal images are proposed. They are named as a likelihood addition method, a likelihood majority method and a Dempster-Shafer's rule method. Basic strategies using these methods are to calculate likelihoods for each temporal data and to combine obtained likelihoods for final classification. These three methods use different combining algorithms. From classification experiments, following results were obtained. The method based on Dempster-Shafer's rule of combination showed about 12% improvement of classification accuracies compared to a conventional method. This method needed about 16% more processing times than that of a conventional method. The other two proposed method showed 1% to 5% increase of classification accuracies. However processing times of these two proposed method showed 1% to 5% increase of classification accuracies. However processing times of these two methods are almost the same with that of a conventional method. Among the newly proposed three methods, the Dempster-Shafer's rule method showed the highest classification accuracies with more processing time than those of other methods.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.228-236
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• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• Transactions of Materials Processing
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• v.17 no.1
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• pp.19-26
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• 2008

In the present work, the ratcheting behavior under uniaxial cyclic loading is analyzed. A comparison between the published and the results from the present model is also included. In order to simulate the ratcheting behavior, Two-Back Stress model is proposed by combining the non-linear Armstrong-Frederick rule and the non-linear Phillips hardening rule based on kinematic hardening equation. It is shown that some ratcheting behaviors can be obtained by adjusting the control material parameters and various evolutions of the kinematic hardening parameter can be obtained by means of simple combination of hardening rules using simple rule of mixtures. The ultimate back stress is also derived for the present combined kinematic hardening models.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.11-19
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• 1996

Recently, fuzzy and neural network techniques have been successfully applied to control of complex and ill-defined system in a wide variety of areas, such as robot, water purification, automatic train operation system and automatic container crane operation system, etc. In this paper, we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feedforward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand, feedforward neural networks provide salient features, such as learning and parallelism. In the proposed neuro-fuzzy controller, the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error backpropagation algorithm as a learning rule, while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally, the effectiveness of the proposed controller is verified through computer simulation of an inverted pendulum system.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1094-1096
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• 1998

An intelligent power transformer protective relaying algorithm based on fuzzy decision-making is proposed. To distinguish external faults with CT saturation, overexcitation and inrush conditions from internal faults, a newly designed fuzzy-rule base is used. The Dempster-Shafer's rule of combition is used for fuzzy inference. A series of the S/W and H/W tests show the proposed protection algorithm has practically sufficient sensitivity and selectivity.