• 한국소음진동공학회논문집
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• 제17권7호
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• pp.662-667
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• 2007

A new neuro-control scheme for active control of structures is proposed. It utilizes lattice pattern of state vector as training data of probabilistic neural network(PNN). Therefore. it is the so-called lattice probabilistic neural network(LPNN). PNN makes control forces by using all the training patterns. Therefore, it takes much time to obtain a control force in application. This inevitably may delay the control action. However. control force of LPNN is calculated by using only the adjacent information of LPNN input. So, the response of LPNN is greatly faster than PNN. The proposed control algorithm is applied for three story building under California and El Centro earthquakes. Also, control results of the LPNN are compared with those of the conventional PNN. The structural responses have been suppressed effectively by the proposed algorithm.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.978-982
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• 2007

A new neuro-control scheme for active control of structures is proposed. It utilizes lattice pattern of state vector as training data of probabilistic neural network (PNN). Therefore, it is the so-called lattice probabilistic neural network (LPNN). PNN makes control forces by using all the training patterns. Therefore, it takes much time to obtain a control force in application. This inevitably may delay the control action. However, control force of LPNN is calculated by using only the adjacent information of LPNN input. So, the response of LPNN is greatly faster than PNN. The proposed control algorithm is applied for one story building under California and El Centro earthquakes. Also, control results of the LPNN are compared with those of the conventional PNN. The structural responses have been suppressed effectively by the proposed algorithm.

• Structural Engineering and Mechanics
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• 제17권3_4호
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• pp.483-492
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• 2004

This paper presents an efficient algorithm for the estimation of damage location and severity in bridge structures using Probabilistic Neural Network (PNN). Generally, the Back Propagation Neural Network (BPNN)-based damage detection methods need a lot of training patterns for neural network learning process and the optimum architecture of a BPNN is selected by trial and error. In this paper, the PNN instead of the conventional BPNN is used as a pattern classifier. The modal properties of damaged structure are somewhat different from those of undamaged one. The basic idea of proposed algorithm is that the PNN classifies a test pattern which consists of the modal characteristics from damaged structure, how close it is to each training pattern which is composed of the modal characteristics from various structural damage cases. In this algorithm, two PNNs are sequentially used. The first PNN estimates the damage location using mode shape and the results of the first PNN are put into the second PNN for the damage severity estimation using natural frequency. The proposed damage assessment algorithm using the PNN is applied to a cable-stayed bridge to verify its applicability.

• 한국해양공학회지
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• 제20권5호
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• pp.1-8
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• 2006

Offshore structures are subjected to wave, wind, and earthquake loads. The failure of offshore structures can cause sea pollution, as well as losses of property and lives. Therefore, safety of the structure is an important issue. The reduction of the dynamic response of offshore towers, subjected wind generated random ocean waves, is a critical problem with respect to serviceability, fatigue life and safety of the structure. In this paper, a structural control method is proposed to control the vibration of offshore structures by the probabilistic neural network (PNN). The state vectors of the structure and control forces are used for training patterns of the PNN, in which control forces are prepared by linear quadratic regulator (LQR) control algorithm. The proposed algorithm is applied to a fixed offshore structure under random ocean waves. Active control of the fixed offshore structure using the PNN control algorithm shows good results.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.83-86
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• 1998

In this paper, a fuzzy model based on the Polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. the new algorithm uses PNN algorithm based on Group Mehtod of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.

• 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.

• 한국전기전자재료학회논문지
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• 제21권5호
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• pp.453-457
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• 2008

In this thesis, piezoelectric properties and sintering properties of PCW-PNN-PZT+0.5 wt%$MnO_2$ ceramics adding $B_2O_3$ after creating the specimens with a general method. The lattice constant from the analysis of crystal structure showed that the crystal structure of ceramic features both rhombohdral and tetragonal structures and that the pychlore structure was decreased with the increase of the sintering temperature. The electromechanical coupling coefficient showed its maximum of 31 % in the sintered specimens at $1050^{\circ}C$, and its minimum of 20 % in the sintered specimens at $1150^{\circ}C$. The mechanical quality coefficient marked the maximum of 139 at the sintering temperature of $1150^{\circ}C$.

• 한국구조물진단유지관리공학회 논문집
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• 제6권4호
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• pp.169-179
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• 2002

This paper presents an efficient algorithm for the estimation of damage location and severity in structure using Probabilistic Neural Network (PNN). Artificial neural network has been being used for damage assessment by many researchers, but there are still some barriers that must be overcome to improve its accuracy and efficiency. The major problems with the conventional neural network are the necessity of many training data for neural network learning and ambiguity in the relation of neural network architecture with convergence of solution. In this paper, PNN is used as a pattern classifier to overcome those problems in the conventional neural network. The basic idea of damage assessment algorithm proposed in this paper is that modal characteristics from a damaged structure are compared with the training patterns which represent the damage in specific element to determine how close it is to training patterns in terms of the probability from PNN. The training pattern that gives a maximum probability implies that the element used in producing the training pattern is considered as a damaged one. The proposed damage assessment algorithm using PNN is applied to a 2-span continuous beam model structure to verify the algorithm.

• 한국구조물진단유지관리공학회 논문집
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• 제11권1호
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• pp.103-112
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• 2007

구조 재료와 시공기술의 발달로 구조물은 높고 길게 설계할 수 있게 되었으나, 그에 따른 진동문제와 사용성에 관한 문제가 발생하였고, 구조물의 과다한 변위는 구조물에 심각한 손상을 발생시켰다. 이러한 구조물의 진동 문제를 해결하기 위하여 본 논문에서는 구조물의 상태벡터와 제어력만으로 구성된 훈련패턴을 기본으로 하여 인공신경망이론과 확률신경망이론을 사용하여 구조물의 진동을 능동제어하는 방법을 제안하였다. 구조물의 제어를 위해 LQR 제어알고리즘을 이용하여 구조물의 상태벡터와 제어력을 구한 후, 상태벡터를 입력으로 제어력을 출력으로 하는 인공신경망과 확률신경망의 훈련패턴을 구성하였다. 제안된 방법을 사용하여 Northridge 지진하중을 받는 3층 빌딩구조물을 제어하였고, 제안된 인공신경망과 확률신경망의 제어 결과를 비교하였다.

• 한국전산구조공학회논문집
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• 제23권6호
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• pp.667-673
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• 2010

Recently, structures are becoming longer and higher because of the developments of new materials and construction techniques. However, such modern structures are more susceptible to excessive structural vibrations which cause deterioration in serviceability and structural safety. A modified probabilistic neural network(MPNN) approach is proposed to reduce the structural vibration. In this study, the global probability density function(PDF) of MPNN is reflected by summing the heterogeneous local PDFs automatically determined in the individual standard deviation of each variable. The proposed algorithm is applied for the vibration control of a three-story shear building model under Northridge earthquake. When the control results of the MPNN are compared with those of conventional PNN to verify the control performance, the MPNN controller proves to be more effective than PNN methods in decreasing the structural responses.