• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.149-151
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• 2005

In this paper, we propose the Optimal Polynomial Neural Networks(PNN) for nonlinear process. The PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and modified quadratic, and is connected as various kinds of multi-variable inputs. The conventional PNN depends on experience of a designer that select No. of input variable, input variable and polynomial type. Therefore it is very difficult a organizing of optimized network. The proposed algorithm identified and selected No. of input variable, input variable and polynomial type by using Genetic Algorithms(GAs). In the sequel the proposed model shows not only superior results to the existing models, but also pliability in organizing of optimal network. Medical Imaging System(MIS) data is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권3호
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• pp.309-330
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• 2009

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권1호
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• 한국지능시스템학회논문지
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• 제15권7호
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• pp.846-851
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• 2005

본 논문은 최적 탐색 알고리즘인 유전자 알고리즘을 이용하여 다항식 뉴럴네트워크(Polynomial Neural Networks : PNN)의 최적 설계가 그 목적이다. 기존의 다항식 뉴럴네트워크는 확장된 GMDH(Group Method of Data Handling) 방법에 기반을 두며, 네트워크의 성장과정을 통하여 각 층의 다항식뉴런(혹은 노드)에서 고정된 (설계자에 의해 미리 선택된) 노드 입력들의 수뿐만 아니라 다항식 차수(1차, 2차, 그리고 수정된 2차식)를 이용하였다. 더구나, 그 방법은 학습을 통해 생성된 PNN이 최적 네트워크 구조를 가진다는 것을 보증하지 못한다. 그러나, 제안된 GA-based PNN 모델은 다음의 파라미터들- 즉 입력변수의 수, 입력변수, 및 다항식 차수-을 유전자 알고리즘을 이용하여 선택 동조함으로써 그 구조를 구조적으로 더 최적화된 네트워크가 되도록 하고, 기존의 PNN보다 훨씬 더 유연하고, 선호된 뉴럴 네트워크가 되도록 한다. 하중계수를 가진 합성성능지수가 그 모델의 근사화 및 일반화(예측) 능력 사이의 상호 균형을 얻기 위해 제안된다. GA-based PNN의 성능을 평가하기 위해 그 모델은 가스 터빈 발전소의 NOx 배출 공정 데이터로 실험된다. 비교해석은 제안된 GA-based PNN이 앞서 나타난 다른 지능모델보다 더 우수한 예측능력뿐만 아니라 높은 정확성을 가진 모델임을 보인다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.827-829
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• 1999

The identification of nonlinear system using Extended GMDH(EGMDH) is studied in this paper. The proposed EGMDH algorithm is based on GMDH(Group Method of Data handling) method and its structure is similar to Neural Networks. The each node of EGMDH structure utilizes several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. As the operating condition changes, the parameters of EGMDH will also change, so the proposed scheme by means of the EGMDH method is capable of adapting rapidly to the changing environment. The simulation result shows that the simple nonlinear process can be modeled reasonably well by the proposed method which are simple but efficient.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권3호
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• pp.145-156
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• 2000

In this paper, we propose a new methodology which includes the optimal design procedure of Polynomial Neural Networks(PNN) structure for model identification of complex and nonlinear system. The proposed PNN algorithm is based on GMDA(Group Method of Data handling) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. In other words, the PNN uses high-order polynomial as extended type besides quadratic polynomial used in GMDH, and the number of input of its node in each layer depends on that of variables used in the polynomial. The design procedure to obtain an optimal model structure utilizing PNN algorithm is shown in each stage. The study is illustrated with the aid of pH neutralization process data besides representative time series data for gas furnace process used widely for performance comparison, and shows that the proposed PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and prediction capabilities of model is evaluated and also discussed.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.518-521
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• 2007

To retrieve Sea Surface Temperature(SST) from NOAA-AVHRR imagery the spilt window atmospheric correction algorithm is generally used. Recently, there have been various new algorithms developed to process these data, namely the variable-coefficient split-window, the R54 transmittance-ratio method, fixed-coefficient nonlinear algorithm, dynamic water vapour (DWV) correction method, Dynamic Water Vapour and Temperature algorithm (DWVT). We used MCSST (Multi-Channel Sea surface temperature) and NLSST(Non linear sea surface temperature) algorithms in this study. The study area is around the Korea sea area (Yellow Sea). We compared and analyzed with various methods by applying each Ocean in-situ data and satellite data. The primary aim of study is to verify and optimize algorithms. Finally, this study proposes an optimized algorithm for SST retrieval.

• 한국지능시스템학회논문지
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• 제10권4호
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• pp.343-350
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• 2000

본 논문에서는, 최적 시스템을 위해서 FNN과 PNN에 기반을 둔 Multi-FPNN(다중 퍼지 다항식 뉴럴네트워크) 모델을 제안한다. 여기서 FNN 구조는 각각의 분리된 입력변수에 의해 분할된 퍼지 입력공간을 사용해서 설게되고, 간략 퍼지추론 방법과 오류 역전파 알고리즘을 이용한다. FNN은 더 좋은 출력성능을 얻기 위해 PNN과 결합한다. GMDH 방법에 기초한 PNN 구조의 각 노드는 1차 및 2차 고계 다항식의 두 형태를 사용하고, 그 노드의 입력의 입력은 2, 3, 4의 세 종류의 다변수 입력을 사용한다. 그리고 다중 FPNN 모델의 구조와 파라미터를 동정하기 위햐 HCM 크러스터링방법과 유전자 알고리즘을 사용한다. 여기서, 시스템을 위해 데이터 전처리 기능을 수행하는 HCM 클러스터링 방법은 입출력 공간분할에 의해 다중 FPNN 구조를 결정하기 위해 사용된다. 모델의 근사화와 일반화 능력 사이에 충분한 군형을 ？기 위해 하중계수를 가진 합성 성능지수(목적함수)를 사용한다. 데이터 개수, 비선형의 정도(입.출력 데이터 분포)에 위존하는 이 합성 목적함수의 하중계수의 선택 및 조절을 통하여 최적의 다중 FPNN모델을 설계하는 것이 유용하고 효과적임을 보인다. 본 연구는 두 개의 대표적 수치예의 도움으로 설명되고, 그 모델의 근사화 및 일만화 능력에 관련된 합성 성능 지수가 평가되고, 도한 토의된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.152-154
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• 2005

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed genetic algorithms-based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권7호
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.