• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.102.1-102
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• 2002

In this paper, we propose Self-organizing neurofuzzy networks(SONFN) and discuss their comprehensive design methodology. The proposed SONFN is generated from the mutually combined structure of both neurofuzzy networks (NFN) and polynomial neural networks(PNN) for model identification of complex and nonlinear systems. NFN contributes to the formation of the premise part of the SONFN. The consequence part of the SONFN is designed using PNN. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. We discuss two kinds of SONFN architectures and propose a comprehensive learning algorithm. It is shown that this network...

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 추계종합학술대회
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• pp.265-270
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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 in the speech, the gesture, the physiological phenomena(the breath, the beating of the pulse, etc). In this paper, the method to have cognizance of emotion from anyone's voice signals is presented and simulated by using neuro-fuzzy model.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.853-856
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• 1993

In this paper, we describe a chaos simulator as a developing tool for applications of chaos engineering. This simulator is composed of three modules, such as generation module of chaotic signals by deterministic rules, determination module whether observed time series is chaos or not, and nonlinear system identification module by self generating Neuro Fuzzy Model.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.279-282
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• 2002

본 논문에서는 뉴로-퍼지 모델에서 입력 공간의 효율적인 분할을 위하여 계층적 클러스터링방법을 이용하고 있다. 기존의 HCM, FCM 등에서 초기치를 임의로 선택함으로써 데이터의 클러스터를 생성하였으나 제안된 방법은 계층적인 클러스터링을 이용하여 각 데이터간의 정보를 이용하여 클러스터링을 좀더 일반화하였다. 임의로 주어진 초기치에 의하여 클러스터의 형태가 바뀔 수 있는 문제점을 각각의 데이터 정보를 이용함으로써 이러한 문제를 해결하고자 하였다. 이를 자동차 연료 예측 문제에 적용하여 제안된 방법의 유용성을 보이고자 한다.

• 한국ITS학회 논문지
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• 제6권2호
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• pp.45-56
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• 2007

미래 사회로 갈수록 대중교통을 연계하기 위한 환승센터의 기능 및 규모가 확대됨에 따라 환승센터 내에서 이용자를 대상으로 경로를 안내하는 시스템의 필요성이 제기되고 있다. 그러나 도로에서의 차량을 대상으로 한 최적경로에 관한 모형은 많이 제시되고 있는 반면 건물 내에서 보행자를 위한 최적경로에 대한 연구는 미미하다. 따라서 본 연구는 복합환승센터 내에서 승용차를 이용하는 개인이 지하주차장에서 환승센터 내 대중교통 수단 이용을 위한 승강장이나 상가 시설로 이동하는 최적의 경로를 제시할 수 있는 모형개발을 주요내용으로 하였다. 건물 내 최적경로 모형은 다익스트라(Dijkstra) 알고리즘을 기본으로 하여 거리의 최소비용을 산정하고, 여기에 이동시간, 피로감, 쾌적도와 대기시간 등의 요소를 순위합, 산술합 방법 등을 통해 적용하여 객관성을 부가하였다. 또한 대중교통 수단의 이용자들의 성향이 반영된 Neuro-Fuzzy 모형을 통하여 환승센터 내 환승하는 이용자에게 최적의 교통수단을 제시하고, 그 해당 수단의 승강장까지 최적경로를 제시함으로써 모형의 효율성을 높였다. 마지막으로 가상의 시나리오들를 통하여 개발 모형의 효율성 검증하였다. 검증결과 개발모형을 통했을 경우 그렇지 않을 경우보다, 수직이동 경로차이의 시나리오에서 약 75%, 수평이동 경로차이의 시나리오에서 약 $24.5{\sim}107.7%$ 더 효율적으로 나타났다.

• 한국지능시스템학회논문지
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• 제25권2호
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• pp.119-125
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• 2015

정수장, 하수처리장, 폐수처리장의 배출수 처리공정에서 고 농도의 슬러지 선별, 이송 및 약품 투입량 조절을 위한 기준으로 슬러지 농도계가 사용되고 있다. 그러나 슬러지에 함유된 이물질이 혼입될 경우 감쇄량이 증가하거나 초음파가 수신부에 전달되지 않아 실제 농도값 보다 높은 값을 출력하거나 헌팅현상이 발생한다. 또한 단일 센서에 슬러지 포착 또는 고장 등의 문제로 배출수 공정 자동화에 어려움이 많았다. 이러한 문제점을 개선하기 위해 초음파 다중빔 농도계를 개발하여 사용하고 있으나 특정 초음파 빔의 농도 측정값에 오류가 발생할 경우 전체 농도시스템의 성능이 떨어지는 단점이 있다. 따라서 본 논문에서는 초음파 다중빔 농도계 간의 신뢰성을 판단하고, 신뢰성이 높은 다중빔 농도계만을 사용하여 슬러지 농도 예측값의 성능 향상방안을 제시하였다. 예측 알고리즘으로는 뉴로-퍼지모델을 적용하였으며 다양한 실험을 통하여 제안된 방법의 타당성을 검증하였다.

• 전기학회논문지
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• 제63권4호
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• pp.526-533
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• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.

• Journal of Power Electronics
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• 제13권4호
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권1호
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• pp.27-32
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• 2006

Verbal communication is the most commonly used mean of communication. A spoken word carries a lot of informations about speakers and their emotional states. In this paper we designed a model to recognize emotional states in a speech, a first phase of two phases in developing a toy machine that recognizes emotional states in a speech. We conducted an experiment to extract and analyse the emotional state of a speaker in relation with speech. To analyse the signal output we referred to three characteristics of sound as vector inputs and they are the followings: frequency, intensity, and period of tones. Also we made use of eight basic emotional parameters: surprise, anger, sadness, expectancy, acceptance, joy, hate, and fear which were portrayed by five selected students. In order to facilitate the differentiation of each spectrum features, we used the wavelet transform analysis. We applied ANFIS (Adaptive Neuro Fuzzy Inference System) in designing an emotion recognition model from a speech. In our findings, inference error was about 10%. The result of our experiment reveals that about 85% of the model applied is effective and reliable.