• 제어로봇시스템학회논문지
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• 제10권3호
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• pp.280-285
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• 2004

This paper is concerned with the modeling and identification of sequencing batch reactor (SBR) via genetic algorithm based polynomial neural network (GA-based PNN). The model describes a biological SBR used in the wastewater treatment process fur nitrogen removal. A conventional polynomial neural network (PNN) is applied to construct a predictive model of SBR process fur nitrogen removal before. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must go with heavy computation burden and low efficiency. To alleviate these problems, we propose GA-based PNN. The order of the polynomial, the number of input variables, and the optimum input variables are encoded as a chromosome and fitness of each chromosome is computed. Simulation results have shown that the complex SBR process can be modeled reasonably well by the present scheme with a much simpler structure compared with the conventional PNN model.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.191-194
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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 PW 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 터빈발전소의 NOx 배출 공정 데이터로 실험된다. 비교해석은 제안된 GA-based PNN이 앞서 나타난 다른 지능모델보다 더 우수한 예측능력뿐만 아니라 높은 정확성을 가진 모델임을 보인다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2006년도 추계학술대회 논문집 Vol.19
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• pp.32-33
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• 2006

In this study, in order to develop multilayer ceramic actuator for ultrasonic nozzle and ultrasonic vibrator, PMN-PNN-PZT ceramics were fabricated using $Li_2CO_3$. $Na_2CO_3$ and ZnO as sintering aids. And then, their piezoelectric and dielectric properties according to the amount of ZnO addition were investigated. The addition of ZnO improved density, dielectric constant, electromechanical coupling factor, mechanical quality factor and piezoelectric d constant of PMN-PNN-PZT ceramics due to the increase of sinterability and accepter doping effect. Electromechanical coupling factor and mechanical quality factor of PMN-PNN-PZT ceramics increased with ZnO amount up to 0.4wt% and then decreased. At the sintering temperature of $900^{\circ}C$ and 0.4wt% ZnO addition, density, dielectric constant, electromechanical coupling factor, mechanical quality factor and piezoelectric d constant showed the optimum value of 7.876g/$cm^2$, 1299, 0.612, 1151 and 369pC/N, respectively.

• International Journal of Concrete Structures and Materials
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• 제19권1E호
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2005년도 추계학술대회 논문집 Vol.18
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• pp.212-213
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• 2005

In this study, in order to develop low temperature sintering piezoelectric actuator, $Pb_{0.985}Bi_{0.01}(Mg_{1/2}W_{1/2})_{0.03}(Ni_{1/3}Nb_{2/3})_{0.13}(Zr_{0.50},Ti_{0.50})_{0.84}$ (PMW-PNN-PZT) ceramic systems were fabricated using $CaCO_3-Li_2CO_3$, sintering aid through a post-annealing process. The sinterability of PMW-PNN-PZT ceranics was remarkably enhanced by liquid phase sintering of $CaCO_3$ and $Li_2CO_3$. But, it was confimed form the X-ray diffraction pattern that the secondary phase along grain boundaries, deteriorated the piezoelectric properties. The secondary phase along grain boundaries was significantly removed by annealing after sintering. The 0.2wt% $Li_2CO_3$-0.25wt% $CaCO_3$-added PMW-PNN-PZT ceramics post-annealed at 900$^{\circ}C$ for 90min exhibited the excellent electromechanical coupling factor($k_p$) of 63.3% and piezoelectric constant($d_{33}$) of 452pC/N, respectively, for multilayer piezoelectricactuatorapplication.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권9호
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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.

• 한국전기전자재료학회논문지
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• 제29권6호
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• pp.332-336
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• 2016

In this study, in order to develop the composition ceramics for ultrasonic sensor with high $d_{33}*g_{33}$, $Pb_{1-3x/2}Bix(Mg_{1/2}W_{1/2})_{0.03}(Ni_{1/3}Nb_{2/3})_{0.09}(Zr_{0.5}Ti_{0.5})_{0.88}O_3$(PMW-PNN-PZT) system ceramics were prepared using CuO as sintering aids. And then, their microstructure, piezoelectric and dielectric characteristics were systemetically investigated with bismuth substitution. The PMW-PNN-PZT ceramic specimens could be sintered at sintering temperature of $940^{\circ}C$ by adding sintering aids. At x=0.015 specimen, the density, electromechanical coupling factor($k_p$), dielectric constant, piezoelectric constant($d_{33}$) and piezoelectric figure of merit($d_{33}*g_{33}$) indicated the optimal properties of $7.90g/cm^3$, 0.67, 2,511, 628 pC/N, and $17.7pm^2/N$, respectively, for duplex ultrasonic sensor application.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2762-2764
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• 2001

In this study, we introduce and investigate a class of dynamic perceptron architectures, discuss a comprehensive design methodology and carry out a series of numeric experiments. The proposed dynamic perceptron architectures are called as Polynomial Neural Networks(PNN). PNN is a flexible neural architecture whose topology is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated on the fly. In this sense, PNN is a self-organizing network. PNN has two kinds of networks, Polynomial Neuron(FPN)-based and Fuzzy Polynomial Neuron(FPN)-based networks, according to a polynomial structure. The essence of the design procedure of PN-based Self-organizing Polynomial Neural Networks(SOPNN) dwells on the Group Method of Data Handling (GMDH) [1]. Each node of the SOPNN exhibits a high level of flexibility and realizes a polynomial type of mapping (linear, quadratic, and cubic) between input and output variables. FPN-based SOPNN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulations involve a series of synthetic as well as experimental data used across various neurofuzzy systems. A detailed comparative analysis is included as well.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1996년도 춘계학술대회 논문집
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• pp.142-145
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• 1996

In this study, dielectric and piezoelectric properties of 0.5PNN-(0.5-x)PZN-xPZT system ceramics with PZT mole ratio were investigated. As the amount of PZT increases, curie temperature was increased. The maximum of dielectric and piezoelectric constant was shoun at 0.3 mole of PZT amount. As a results, we have found that the structure of ceramics with PZT 0.3 mole was morphotropic phase boundary.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 하계학술대회 논문집 C
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• pp.1271-1273
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• 1994

In the field of the optics, precise machine, semiconducting processing, the micro-positioning actuators are required for the control of position in the submicron range. In this study, PNN-PZN-PZT ceramics were fabricated by solid state reaction. The structural, dielectric and electric properties were investigated for sintering condition. The specimen sintered for 1hr at 1,150($^{\circ}C$), had the highest density and dielectric contant.