• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.675-678
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• 1997

In this paper, we present a fast and robust algorithm for the design of fuzzy controller and identifying fuzzy model from numerical data by combining the cluster estimation method with a linear least squares estimation procedure. The proposed method is compared with Adaptive Neuro-Fuzzy Inference System(ANFIS) as the standard example of neuro-fuzzy model. Finally we will show its usefulness and effectiveness for the design of fuzzy controller of a cart-pole system and fuzzy modeling for the coagulant dosing of a water purification system.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.58-66
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• 2007

This paper introduces the new generic dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system for complex dynamic hydrological modeling tasks. The proposed DNFLMS applies a local generalization principle and an one-pass training procedure by using the evolving clustering method to create and update fuzzy local models dynamically and the extended Kalman filtering learning algorithm to optimize the parameters of the consequence part of fuzzy local models. The proposed DNFLMS is applied to develop the inference model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatts Cobb hydropower station on springs flow. It is demonstrated that the proposed DNFLMS is superior in terms of model accuracy, model complexity, and computational efficiency when compared with a multi-layer perceptron trained with the back propagation learning algorithm and well-known adaptive neural-fuzzy inference system, both of which adopt global generalization.

• 한국농공학회논문집
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• 제49권3호
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• pp.21-31
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• 2007

This study is aimed at the development of a runoff forecasting model by using the Fuzzy inference system and Neural Network model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting. The Neuro-Fuzzy (NF) model were used in this study. The NF model, recently received a great deal of attention, improve the existing Neural Networks by the aid of the Fuzzy theory applied to each node. The study area is the downstreams of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model respectively. The schematic diagram method and the statistical analysis are conducted to evaluate the feasibility of rainfall-runoff modeling. The model accuracy was rapidly decreased as the forecasting time became longer. The NF model can give accurate runoff forecasts up to 4 hours ahead in standard above the Determination coefficient $(R^2)$ 0.7. In the comparison of the runoff forecasting using the NF and TANK models, characteristics of peak runoff in the TANK model was higher than ones in the NF models, but peak values of hydrograph in the NF models were similar.

• 한국안전학회지
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• 제37권1호
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• pp.78-87
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• 2022

In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.110.1-110
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• 2002

We propose a novel neuro-fuzzy system based on an efficient clustering method. It is a very useful method that improves the performance of a fuzzy model with small number of fuzzy rules. The fuzzy clustering methods are studied in the wide range of fuzzy modeling. One of them, the grid partition method has problem of exponentially increasing number of rules when the dimension of input or number of membership function is linearly increased. On the other hand, the Expectation Maximization algorithm is an efficient estimation for unknown parameters of the Gaussian mixture model. Here it is noted that the parameters can be used for fuzzy clustering method. In a fuzzy modeling, it is desired that...

• 대한토목학회논문집
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• 제33권5호
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• pp.1809-1827
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• 2013

기후변화로 인해 발생하는 이상홍수에 대비하기 위해서는 다양한 대책을 강구할 필요가 있다. 그 중 비구조적 대책으로 홍수예경보시스템을 구축하여 홍수에 대비할 수 있도록 하는 것이 중요하다. 본 연구의 목적은 실시간 홍수예측 시스템을 구축하기 위해 뉴로-퍼지 모형과 다중선형회귀 모형을 비교하여 우수한 실시간 홍수예측 모형을 개발하는데 있다. 이를 위해 같은 입력자료를 사용하여 뉴로-퍼지 모형과 다중선형회귀 모형을 구축하고 낙동강 유역의 다양한 홍수사상에 대해 적용하였다. 모의결과 뉴로-퍼지 모형이 다중선형회귀 모형보다 좀 더 나은 예측 결과를 나타내는 것을 확인할 수 있었다. 본 연구는 향후 낙동강 유역의 충분한 선행시간을 확보한 정확도 높은 홍수정보시스템의 구축에 활용할 수 있을 것으로 판단된다.

• 한국지능시스템학회논문지
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• 제11권7호
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• pp.611-614
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• 2001

Voice is one of the most efficient communication media and it includes several kinds of factors about speaker, context emotion and so on. Human emotion is expressed is expressed in the speech, the gesture, the physiological phenomena(the breath, the beating of the pulse, etc). In this paper, the emotion recognition method model using neuro-fuzzy in order to have cognizance of emotion from voice signal is presented and simulated.

• 조명전기설비학회논문지
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• 제20권3호
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• pp.53-61
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• 2006

본 논문은 유도전동기 드라이브의 고성능 제어를 위한 적응 퍼지-뉴로 제어기를 제시한다. 이 알고리즘의 설계는 퍼지제어와 신경회로망을 사용하는 퍼지-신경회로망 제어기에 기초한다. 적응 퍼지-뉴로 제어기는 신경회로망의 학습패턴과 같은 퍼지 룰을 사용하고 또한 지령값과 실제값 사이의 오차를 최소화하기 위하여 신경회로망의 뉴런사이의 하중을 역전파 알고리즘 방법을 사용하여 조절한다. 적응 기준 모델 설계는 기준모델의 출력과 전동기 속도 사이의 오차와 오차 변화분을 기초로 한 퍼지 로직에 의하여 실행되는 적응 메카니즘을 제시한다. 적응 퍼지-뉴로 제어기의 제어 성능은 다양한 동작 상태에 대한 분석으로 평가한다. 제안한 제어시스템의 실험 결과는 고성능과 파리미터 변동과 정상상태 정확성, 순시응답의 강인성을 가진다.

• 한국정보통신학회논문지
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• 제5권4호
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• pp.730-737
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• 2001

본 논문에서는 적응 뉴로-퍼지 제어기를 이용하여 비선형 복합시스템 모델의 안정화 제어 방법에 적용한다. 제안된 적응 뉴로-퍼지 제어기는 언어적 퍼지추론, 프로세스의 입출력 데이터를 이용하는 신경회로망, 최적이론 등이 포함된 인공지능을 시스템구조와 파라메터 검증에 필요한 도구로 이용한다. 그 결과 제안된 방법이 이전에 연구되었던 다른 방법보다 아주 높은 인공지능 모델을 제시하였다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2005년도 학술대회 논문집
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.