• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.100-107
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• 2002

The advanced bilateral control algorithm, which can enlarge a reflected force by combining force reflection and compliance control, greatly enhances workability in teleoperation. In this scheme the maximum boundaries of a compliance controller and a force reflection gain guaranteeing stability and good task performance greatly depend upon characteristics of a slave arm, a master arm, and an environment. These characteristics, however, are generally unknown in teleoperation. It is, therefore, very difficult to determine such maximum boundary of the gain. The paper presented a novel method for design of an advanced bilateral controller. The factors affecting task performance and stability in the advanced bilateral controller were analyzed and a design guideline was presented. The neurofuzzy compliance model (NFCM)-based bilateral control proposed herein is an algorithm designed to automatically determine the suitable compliance for a given task or environment. The NFCM, composed of a fuzzy logic controller (FLC) and a rule-learning mechanism, is used as a compliance controller. The FLC generates compliant motions according to contact forces. The rule-learning mechanism, which is based upon the reinforcement learning algorithm, trains the rule-base of the FLC until the given task is done successfully. Since the scheme allows the use of large force reflection gain, it can assure good task performance. Moreover, the scheme does not require any priori knowledge on a slave arm dynamics, a slave arm controller and an environment, and thus, it can be easily applied to the control of any telerobot systems. Through a series of experiments effectiveness of the proposed algorithm has been verified.

• Journal of the Society of Naval Architects of Korea
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• v.35 no.3
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• pp.79-87
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• 1998

The purpose of this paper is to develope a neurofuzzy system for a ship design which can determine efficiently design values e.g. principal dimensions and hull factors in a conceptual design. The neurofuzzy system for a ship design(NeFHull) applies a information about given input-output data to fuzzy theories and deals these fuzzificated values with neural networks, e.g. first, redefines normalized input-output data ad membership functions and then executes these fuzzficated information with backpropagation neural networks. We use a hybrid learning algorithm in the training of neural networks and examine the usefulness of suggested method through mathematical and mechanical examples.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.218-220
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• 2000

In this paper, the problem of the design of an intelligent type precompensator is discussed for the performance improvement of a power system stabilizer(PSS). An advantage of the scheme is that an existing PSS can be easily modified in our control structure simply by adding an adaptive neurofuzzy-based precompensator. The overall system has been tested on a simulation model in different operation conditions. Case studies show the proposed scheme can provide the good damping of the power system over the wide range of operating conditions and improve the dynamic performance of the system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.157.5-157
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• 2001

Generally, the application of Programmable Logic Controller PLC is emphasized on the Process Control. This paper presents Neurofuzzy application, Which can estimate the distance to a fault by means of PLC and based up on the Electrical Power System theory and ground resistance. The case study refers to the distribution lines of the Provincial Electricity Authority (PEA). Also, the thesis is supposed to be of much benefit: saving time both to go to the scene and to clear fault, reducing unpleasant impacts on customers and stabilizing reliability of the distribution lines.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.362-373
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• 2004

In this study, we introduce a noble neurogenetic approach to the design of the fuzzy controller. The design procedure dwells on the use of Computational Intelligence (CI), namely genetic algorithms and neurofuzzy networks (NFN). The crux of the design methodology is based on the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, tuning of the scaling factors of the fuzzy controller is carried out, and then the development of a nonlinear mapping for the scaling factors is realized by using GA based NFN. The developed approach is applied to an inverted pendulum nonlinear system where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis.

• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.12-17
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• 2002

We propose a new category of neurofuzzy networks- Self-organizing Neural Networks(SONN) with fuzzy polynomial neurons(FPNs) and discuss a comprehensive design methodology supporting their development. Two kinds of SONN architectures, namely a basic SONN and a modified SONN architecture are dicussed. Each of them comes with two types such as the generic and the advanced type. SONN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulation involves a series of synthetic as well as experimental data used across various neurofuzzy systems. A comparative analysis is included as well.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.723-732
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• 2001

In this paper, the neurofuzzy controller of optically guided AGV is proposed to improve the path-tracking performance A differential steered AGV has front-side and rear-side optical sensors, which can identify the guiding path. Due to the discontinuity of measured data in optical sensors, optically guided AGVs break away easily from the guiding path and path-tracking performance is being degraded. Whenever the On/Off signals in the optical sensors are generated discontinuously, the motion errors can be measured and updated. After sensing, the variation of motion errors can be estimated continuously by the dead reckoning method according to left/right wheel angular velocity. We define the estimated contour error as the sum of the measured contour in the sensing error and the estimated variation of contour error after sensing. The neurofuzzy system consists of incorporating fuzzy controller and neural network. The center and width of fuzzy membership functions are adaptively adjusted by back-propagation learning to minimize th estimated contour error. The proposed control system can be compared with the traditional fuzzy control and decision system in their network structure and learning ability. The proposed control strategy is experience through simulated model to check the performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.77-78
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• 2005

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.237-240
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• 1997

This paper is to arrange systematically the geometrical & physical data for real ships and to develop the graphic user interface program for initial hull design using NFHFD, which save the distributed information about hull form database and can output multi-variables.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.382-388
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• 2011

Being aware of the risk in advance necessitates intricate processes but is feasible. Although previous studies have demonstrated high accuracy, their performance still leaves room for improvement. A self-organizing feature map (SOM) based neurofuzzy model is developed in this study to provide another alternative for forecasting corporate financial distress. The model is designed to yield high prediction accuracy, as well as reference rules for evaluating corporate financial status. As a database, the study collects all financial reports from listed construction companies during the latest decade, resulting in over 1000 effective samples. The proportion of "failed" and "non-failed" companies is approximately 1:2. Each financial report is comprised of 25 ratios which are set as the input variable s. The proposed model integrates the concepts of pattern classification, fuzzy modeling and SOM-based optimization to predict corporate financial distress. The results exhibit a high accuracy rate at 85.1%. This model outperforms previous tools. A total of 97 rules are extracted from the proposed model which can be also used as reference for construction practitioners. Users may easily identify their corporate financial status by using these rules.