• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.1112_1113
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• 2009

This paper proposes the method to predict the wind turbine power generation using logic-based fuzzy neural networks. To predict the wind turbine power generation neural networks, logic-based fuzzy neural networks, and fuzzy neural models have been considered. But the model considered in this paper can predict the wind turbine power generation with a less complex structure. The simulation results show the effectiveness of the proposed method.

• 전기학회논문지
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• 제56권7호
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• pp.1315-1320
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• 2007

In this paper, we introduce a new fuzzy model called fuzzy combined polynomial neural networks, which are based on the representative fuzzy model named polynomial fuzzy model. In the design procedure of the proposed fuzzy model, the coefficients on consequent parts are estimated by using not general least square estimation algorithm that is a sort of global learning algorithm but weighted least square estimation algorithm, a sort of local learning algorithm. We are able to adopt various type of structures as the consequent part of fuzzy model when using a local learning algorithm. Among various structures, we select Polynomial Neural Networks which have nonlinear characteristic and the final result of which is a complex mathematical polynomial. The approximation ability of the proposed model can be improved using Polynomial Neural Networks as the consequent part.

• 대한산업공학회지
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• 제27권3호
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• pp.305-314
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• 2001

In this paper, we construct an automatic evaluation system of psychological feeling for corporate identity (CI) symbol mark based on a fuzzy neural network technique. The system is modelled by trainable fuzzy inference rules with several input variables (qualitative and quantitative design components of CI symbol mark) and a single output variable (consumer's feeling). The back propagation learning algorithm, which is a conventional learning method of multilayer feedforward neural networks, is used for parameter identification of the fuzzy inference system. The learning ability to train data and the generalization ability to test data are evaluated for the proposed evaluation system by computer simulations.

• 한국산업융합학회 논문집
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• 제17권2호
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• pp.69-76
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• 2014

This paper proposes a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.803-805
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• 2000

In this paper, we propose fuzzy-neural Networks(FNN) which is useful for identification algorithms. The proposed FNN model consists of two steps: the first step, which determines premise and consequent parameters approximately using FCM_RI method, the second step, which adjusts the premise and consequent parameters more precisely by gradient descent algorithm. The FCM_RI algorithm consists FCM clustering algorithm and Recursive least squared(RLS) method, this divides the input space more efficiently than convention methods by taking into consideration correlations between components of sample data. To evaluate the performance of the proposed FNN model, we use the time series data for gas furnace.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2681-2683
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• 2005

In this paper, we present the structure of fuzzy neural network(FNN) based on wavelet function, and apply this network structure to the identification of nonlinear systems. For adjusting the shape of membership function and the connection weights, the parameter learning method based on the gradient descent scheme is adopted. And an approach that uses adaptive learning rates is driven via a Lyapunov stability analysis to guarantee the fast convergence. Finally, to verify the efficiency of our network structure. we compare the Identification performance of proposed wavelet based fuzzy neural network(WFNN) with those of the FNN, the wavelet fuzzy model(WFM) and the wavelet neural network(WNN) through the computer simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 D
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• pp.2873-2875
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• 2005

The optimal structure of the conventional Fuzzy Polynomial Neural Networks (FPNN)[3] depends on experience of designer. For the conventional Fuzzy Polynomial Neural Networks, input variable number, number of input variable, number of Membership Functions(MFs) and consequence structures are selected through the experience of a model designer iteratively. In this paper, we propose the new design methodology to find the optimal structure of Fuzzy Polymomial Neural Network by using Genetic Algorithms(GAs)[4, 5]. In the sequel, It is shown that the proposed Advanced Genetic Algorithms based Fuzzy Polynomial Neural Network(Advanced GAs-based FPNN) is more useful and effective than the existing models for nonlinear process. We used Medical Imaging System(MIS)[6] data to evaluate the performance of the proposed model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1834-1835
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• 2006

A robust adaptive position control algorithm is proposed for servomotor drive system with uncertainties and load disturbance. The proposed controller is comprised of a nominal controller and a robust control. The nominal controller is designed in the condition without all the external load disturbance, nonlinear friction and unpredicted uncertainties. The robust controller containing lumped uncertainty approximator using fuzzy-neural network(FNN) is designed to dispel the effect of uncertainties and load disturbance. The interconnection weight of the FNN can be online tuned in the sense of the Lyapunov stability theorem thus asymptotic stability of the proposed control system can be guaranteed. Finally, simulation results verify that the proposed control algorithm can achieve favorable tracking performance for the induction servomotor drive system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1858-1859
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• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The gradient descendant method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

• 전기저널
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• 통권284호
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• pp.60-65
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• 2000

가전제품은 최근 수년 사이에 급격한 사회정세의 변화와 더불어 소비자의식에 대폭적인 변혁을 가져온 가운데 적절한 가격의 납득이 가는 진짜상품이 요구되는 추세이다. 한편 가전제품에서의 센서(Sensor)기술은, 참으로 성능쇄신의 요소기술을 담당하는 중요한 기술로 센서 그 자체의 고도화$\cdot$다양화에 더하여 마이크로 프로세서를 주체로 하는 지적인 신호처리에 의한 기능과 성능의 향상이 눈부신 바 있으며, 이 두 수레바퀴에 의하여 가전제품용 센서의 인텔리전트시스템이 구성되고 있다. 본고에서는 후자의 신호처리에 의한 인텔리전트화 기술을 지적제어 처리로 간주하여, 센싱 기술에서의 지적제어처리의 자리매김과 구체적인 가전품에서의 응용 예로서 세탁기에 지적제어처리를 탑재하여 기능 향상을 도모한 개발사례를 중심으로 그 개요를 소개한다. 최근의 개발사례로서 세탁기, 에어컨, 냉장고, 청소기 등에 퍼지제어나 뉴럴네트워크, 또한 비선형 처리 등의 응용 예를 표로서 나타내었다. 특히 세탁기에서는 모터의 회전수를 검출하는 회전센서출력의 처리에 의해 다음의 두가지 센싱시스템을 개발하였다. (1) 부하량 검지 시스템 무단계 부하량 검출을 실현하여 검출오차를 약 1/3로 저감시킴과 동시에 에너지 절약(물$\cdot$세제$\cdot$시간)을 도모한다. (2) 언밸런스 건지 시스템 속도감속 성분량 추출에 의한 검출정도 향상과 현행센서 삭제에 의한 코스트 저감을 이룬다.