• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.96.2-96
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• 2001

This paper presents a control method for the experimental mobile vehicle. By merging the advantages of neural network, adaptive and fuzzy control, neural network-based adaptive fuzzy control is proposed. It can deal with a large amount of training data by neural network, from these data producing more accurate fuzzy rules by adaptive control, and then controlling the object by fuzzy control. This is not the simple combination of the three methods, but merging them into one control system Experiments and some future considerations are given.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.648-650
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• 1998

This paper proposes a constructive algorithm for generating the Takagi-Sugeno type fuzzy model through the sequential learning from training data set. The proposed algorithm has a two-stage learning scheme that performs both structure and parameter learning simultaneously. The structure learning constructs fuzzy model using two growth criteria to assign new fuzzy rules for given observation data. The parameter learning adjusts the parameters of existing fuzzy rules using the LMS rule. To evaluate the performance of the proposed fuzzy modeling approach, well-known benchmark is used in simulation and compares it with other modeling approaches.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_1
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• pp.249-256
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• 2024

Traditional methods for monitoring targets rely heavily on probabilistic data association (PDA) or Kalman filtering. However, achieving optimal performance in a densely congested tracking environment proves challenging due to factors such as the complexities of measurement, mathematical simplification, and combined target detection for the tracking association problem. This article analyzes a target tracking problem through the lens of fuzzy logic theory, identifies the fuzzy rules that a fuzzy tracker employs, and designs the tracker utilizing fuzzy rules and Kalman filtering.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.2
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• pp.231-238
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• 1993

A rules of fuzzy control is to represent an expert‘s and engineer‘s ambiguous control knowledge of system with some lingustic rules. This rule is very difficult to represent perfectly because expert‘s knowledge is not precise and the rule is not perfect. We propose the fuzzy reasoning and learning to upgrade precision of imperfect rules successively after system running. In the proposed method, the precision of the backward part of a fuzzy rule is improved by back propagation learning method. Also, the method reasons the compatibility degree of the forward part of fuzzy rule by associative memory method. This method this is successfully applied to design auto-parking fuzzy controller in which expert‘s technology and knowledge are required in the limited area.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.160-166
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• 2005

In this paper, to overcome drawbacks of FLC a self tuning fuzzy sliding mode controller is proposed, which controls the position of excavator's attachment, which can be regarded as an ill-defined system. It is reported that fuzzy logic theory is especially useful in the control of ill-defined system. It is important in the design of a FLC to derive control rules in which the system's dynamic characteristics are taken into account. Control rules are usually established using trial and error methods. However, in the case where the dynamic characteristics vary with operating conditions, as in the operation of excavator attachment, it is difficult to find out control rules in which all the working condition parameters are considered. Experiments are carried out on a test bed which is built around a commercial Hyundai HX-60W hydraulic excavator. The experimental results show that both alleviation of chattering and performance are achieved. Fuzzy rules are easily obtained by using the proposed method and good performance in the following the desired trajectory is achieved. In summary, the proposed controller is very effective control method for the position control of the excavator's attachment.

• Journal of Mechanical Science and Technology
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• v.15 no.5
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• pp.580-591
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• 2001

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.832-839
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• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.705-711
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• 2013

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.123-140
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• 2009

Fuzzy neural network is an integrated model of artificial neural network and fuzzy system and it has been successfully applied in control and forecasting area. Recently ANFIS(Adaptive Network-based Fuzzy Inference System) has been noticed widely among various fuzzy neural network models because of outstanding performance of control and forecasting accuracy. ANFIS has capability to refine its fuzzy rules interactively with human expert. In particular, when we use initial rule structure for machine learning which is generated from human expert, it is highly probable to reach global optimum solution as well as shorten time to convergence. We propose metrics to evaluate interpretability of generated rules as a means of acquiring domain knowledge and compare level of interpretability of ANFIS fuzzy rules to those of C5.0 classification rules. The proposed metrics also can be used to evaluate capability of rule generation for the various machine learning methods.