• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.277-282
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• 2003

In this paper we propose Using Higher Order Neuron on the Supervised Learning Machine of the Kohonen Feature Map. The architecture of proposed model adopts the higher order neuron in the input layer of Kohonen Feature Map as a Supervised Learning Machine. It is able to estimate boundary on input pattern space because or the higher order neuron. However, it suffers from a problem that the number of neuron weight increases because of the higher order neuron in the input layer. In this time, we solved this problem by placing the second order neuron among the higher order neuron. The feature of the higher order neuron can be mapped similar inputs on the Kohonen Feature Map. It also is the network with topological mapping. We have simulated the proposed model in respect of the recognition rate by XOR problem, discrimination of 20 alphabet patterns, Mirror Symmetry problem, and numerical letters Pattern Problem.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.9
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• pp.430-438
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• 2001

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.861-865
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• 1994

By using Neural Networks, a 8$\times$8 Banyan network controller is designed and implemented. In order to solve internal blocking and output blocking, Winner-Take-All method is used. The longer queue takes higher priority. First-in-first-out method is used among the non-blocking cells in the queue selected.The required time to select a cell is 2.7 $\mu$sec for 155Mbps. The implemented controller using Xilinx FPGA chip selects cells within 2.5$\mu$sec.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2656-2659
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• 2001

본 논문은 교사 있는 학습기의 Kohonen Feature Map에 고차 뉴런을 도입, 고차 뉴런을 이용한 Kohonen의 자기 조직화 맵을 제안한다. 일반적인 Kohonen Feature Map의 특징은 입력신호를 받아 출력 면(Kohonen Feature Map) 내의 특정한 위치 주위에 집중하는 메커니즘으로 즉, 국소집중 반응을 구하는 구조이다. 본 논문에서는 종래형의 Kohonen Feature Map의 특징을 보유하며 교사 있는 학습기의 Kohonen Feature Map에 고차 뉴런을 도입하여 국소집중반응 및 특징 축출이 용이하도록 네트워크 구조를 개선한 것이다. 특히, 일차 뉴런의 문제점인 비선형 분리 문제에 대하여 교사 있는 학습기의 Kohonen Feature Map의 입력층에 고차 뉴런을 도입함으로 비선형 분리 가능한 형태의 네트워크 구조로 형성하였다. 그러나, 일반적인 고차 뉴런의 문제점을 보안하기 위해 본 논문에서는 오직 2차 뉴런만을 생성하였으며 중복되는 뉴런을 최대한 억제하였다. 본 제안 모델의 특성을 살펴보기 위해 XOR문제와 20개의 Alphabet을 식별하는 패턴인식 시뮬레이션을 했으며, 본 제안 모델의 범화능력을 알아보기 위하여 Mirror Symmetry를 사용하여 계산기 시뮬레이션을 했다. 그 결과, 본 제안 모델이 종래형의 네트워크 구조보다 뛰어난 인식률을 얻을 수 있었다.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2516-2518
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• 2003

본 논문은 고차 뉴런의 문제점으로 지적되고 있는 뉴런이 방대하게 증가하는 문제를 해결하고자, 최적의 뉴런을 생성하고 생성되어진 고차 뉴런 중 일정 비율로 뉴런의 연결강도를 도태시켜 감에 따라 네트워크상에 나타나는 특성을 비교하였다. 본 논문은 고차 뉴런을 이용한 Kohonen의 자기 조직화 맵의 고차 뉴런부에 일정 비율로 연결강도를 도태한 후 인식률을 얻는 형태로 시뮬레이션을 하였다. 특히, 종래 형태의 고차 뉴런을 이용한 Kohonen 자기 조직화 맵의 알고리즘을 변형없이 사용하였으며 중복되는 뉴런을 최대한 억제하기 위해 2차 뉴런만을 생성한 네트워크 구조 위에 입력 데이터의 특징을 유지하고 고차 뉴런의 특징을 더욱 활성화하기 위해 일정한 양의 연결강도를 도태시킴으로써 출력면에서 국소집중 반응에 의한 정확한 인식률 향상 등을 조사하는 시뮬레이션을 하였다. 본 제안 모델의 특성을 살펴보기 위해 60개의 데이터로 이루어진 금속 소나 음데이터와 암석 소나 음 데이터를 이용하여 금속인지 암석인지를 판별하는 시뮬레이션을 하였다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Photoscience
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• v.6 no.2
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• pp.77-83
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• 1999

Calcium has a variety of functions in neuron and muscle cells and blood clotting, especially in the visual system where dark adapted rods cotransport with Na$\^$+/ into the cell. An influx of Ca$\^$++/ flows out of the cell through the Na$\^$+/-Ca$\^$++/ exchanger. By using a modified Using chamber in order to bring in vivo environment close, we have known that Ca$\^$++/ blocks the activity of guanylate cyclase, in consequence, having an effect on the amplitude of electroretinogram (ERG). We have measured the Ca$\^$++/, K$\^$+/, and Na$\^$+/ concentration in dark and light adapted bullfrog＇s (Rana catesbeiana) vitreous humor. The calcium concentration of the light adapted bullfrog＇s vitreous humor was higher than that of the dark adapted bullfrog＇s vitreous humor This means that ion activity between the photoreceptor and vitreous humor side is light dependent and we have found that a Ca$\^$++/ channel and Ca$\^$++/K$\^$+/ exchanger exist in the vitreous humor side. Taken together permeability of Ca$\^$++/, K$\^$+/ and K$\^$+/ ion internal limiting membrane faced in the vitreous humor side has light-dependent activity during the illumination.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.