• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.2075-2076
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• 2006

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Backpropagation(BP). The conventional BP does not guarantee that the BP generated through learning has the optimal network architecture. But the proposed GA-based BP enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional BP. The experimental results in BP neural network optimization show that this algorithm can effectively avoid BP network converging to local optimum. It is found by comparison that the improved genetic algorithm can almost avoid the trap of local optimum and effectively improve the convergent speed.

• 한국멀티미디어학회논문지
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• 제21권4호
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• pp.471-477
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• 2018

As an important key technology using on electronic devices, face recognition has become one of the hottest technology recently. The traditional BP Neural network has a strong ability of self-learning, adaptive and powerful non-linear mapping but it also has disadvantages such as slow convergence speed, easy to be traversed in the training process and easy to fall into local minimum points. So we come up with an algorithm based on BP neural network but also combined with the PCA algorithm and other methods such as the elastic gradient descent method which can improve the original network to try to improve the whole recognition efficiency and has the advantages of both PCA algorithm and BP neural network.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.175-186
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• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

• 한국기계가공학회지
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• 제14권3호
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• pp.65-73
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• 2015

This paper presents a method of predicting the machining parameters on the turning process of low carbon steel using a neural network with back propagation (BP) and particle swarm optimization (PSO). Cutting speed, feed rate, and depth of cut are used as input variables, while surface roughness and electric current consumption are used as output variables. The data from experiments are used to train the neural network that uses BP and PSO to update the weights in the neural network. After training, the neural network model is run using test data, and the results using BP and PSO are compared with each other.

• Molecules and Cells
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• 제37권9호
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• pp.691-698
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• 2014

SCYL1-BP1 is thought to function in the p53 pathway through Mdm2 and hPirh2, and mutations in SCYL1-BP1 are associated with premature aging syndromes such as Geroderma Osteodysplasticum; however, these mechanisms are unclear. Here, we report significant alterations in miRNA expression levels when SCYL1-BP1 expression was inhibited by RNA interference in HEK293T cells. We functionally characterized the effects of potential kernel miRNA-target genes by miRNA-target network and protein-protein interaction network analysis. Importantly, we showed the diminished SCYL1-BP1 dramatically reduced the expression levels of EEA1, BMPR2 and BRCA2 in HEK293T cells. Thus, we infer that SCYL1-BP1 plays a critical function in HEK293T cell development and directly regulates miRNA-target genes, including, but not limited to, EEA1, BMPR2, and BRCA2, suggesting a new strategy for investigating the molecular mechanism of SCYL1-BP1.

• 융합신호처리학회논문지
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• 제10권3호
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• pp.164-169
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• 2009

본 논문에서는 고해상도 위성영상에 대해서 분할된 후보영역의 텍스처 정보를 기반으로 BP 신경회로망을 이용한 도로영역검출방법을 제안한다. 먼저, N.Otsu가 제안한 히스토그램 기반의 이진화와 열림연산을 수행하여 배경영역으로부터 일차적으로 도로영역인 전경부분을 분할한다. 그리고 전경부분의 색상 히스토그램을 이용하여 주요색상을 추출한 후 ${\pm}25$ 범위 이내에 있는 영역을 도로영역 후보를 검출한다. 마지막으로, 분할된 후보 도로영역에 대해서 동시발생행렬을 이용하여 텍스처 정보를 추출한 후 BP 신경회로망을 이용하여 최종적인 도로영역을 검출한다. 제안한 방법은 도로영역이 일정한 밝기값과 형태를 가진다는 사실에 착안한 것으로, 실험에서 다양한 위성영상들을 대상으로 평균 90% 이상의 검출율을 보여 그 유효함을 보였다.

• 한국전자통신학회논문지
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• 제17권6호
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• pp.1129-1136
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• 2022

본 연구는 RSSI의 거리측정 방법이 외부 환경에 의해 쉽게 영향을 받아 위치 오차가 크다는 결점을 도출하였고 이 3차원 배치 환경에서 RSSI의 거리측정 노드에서 측정한 거리값을 최적화하는 문제에 대해 향상된 CA-PSO 알고리즘을 개선한 CA-PSO-BP 알고리즘을 제안하였다. 제안된 알고리즘은 3차원 무선센서네트워크(WSN) 공간에서 인식할 수 없는 노드를 설정할 수 있도록 하였다. 또한, CA-PSO를 BP 신경망에 응용하므로, 학습을 통해 BP 네트워크의 학습시간 단축과 알고리즘의 수렴 속도를 제고 할 수 있었다. 본 연구에서 제안한 알고리즘을 통해 네트워크의 위치의 정밀도를 현저(15%)하게 높일 수 있다는 것을 증명하였고 유의미한 결과를 얻을 수 있었다.

• 한국수자원학회논문집
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• 제33권4호
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• pp.505-515
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• 2000

본 연구는 하천에서 호우의 발생에 따라 하천 유출수문곡선을 예측코자 블랙박스모형의 신경망이론을 적용하여 수문학적인 문제를 규명하고자 하였다. 이를 위해 신경망 이론 중 Levenverg-Marquardt 방법에 의한 오차역전파 알고리즘과 Radial Basis Function Network(RBFN)를 이용하여 IHP 대표유역인 보청청유역에 수문곡선을 적용하여 선행유출량 예측과 미학습 유역의 적용성을 검토하였다. 그 결과 복잡하고 비선형적인 수문계의 강우-유출 과정의 학습에 있어 RBFN은 은닉층에서 자율학습, 출력층에서 지도학습의 두 단계로 나누어 학습을 함으로서 BP 알고리즘보다 학습시간이 빠르게 나타났고, 선행유출량의 예측결과 여러 통계적 지표에서 RBFN이 BP 알고리즘보다 좋은 결과를 얻을 수 있었다. 미학습 유역의 적용성 검토에서도 BP알고리즘과 RBFN 모두 첨두치가 비교적 실측자료의 경향과 비슷한 경향으로 나타났다.

• 한국기계가공학회지
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• 제18권9호
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

• Steel and Composite Structures
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• 제28권5호
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• pp.517-525
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• 2018

The specimens made by Z2CND18.12N austenitic stainless steel were conducted on a 100 kN closed loop servo hydraulic tension-compression testing machine with a digital controller. Uniaxial tension and uniaxial ratcheting effect tests were carried out at $25^{\circ}C$. Moreover, Uniaxial tension tests were conducted at $150^{\circ}C$, $250^{\circ}C$ and $350^{\circ}C$. Based on these experimental data, the prediction models of stress-strain curve and the relationship of ratcheting strain and number of cycles were established by the algorithm principle of BP neural network. The results indicated that the predicted results of neural network model were in well agreement with experimental data. It was found that the BP neural network model had high validity and accuracy.