• 디지털콘텐츠학회 논문지
• /
• 제8권3호
• /
• pp.393-401
• /
• 2007

P2P systems have gained a lot of research interests and popularity over the years and have the capability to unleash and distribute awesome amounts of computing power, storage and bandwidths currently languishing - often underutilized - within corporate enterprises and every Internet connected home in the world. Since there is no central control over resources or devices and no before hand information about the resources or devices, device discovery remains a substantial problem in P2P environment. In this paper, we cover some of the current solutions to this problem and then propose our feed forward neural network (FFNN) based solution for device discovery in mobile P2P environment. We implements feed forward neural network (FFNN) trained with back propagation (BP) algorithm for device discovery and show, how large computation task can be distributed among such devices using agent technology. It also shows the possibility to use our architecture in home networking where devices have less storage capacity.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
• /
• pp.425-428
• /
• 2003

In this paper, the stabilization of an inverted pendulum system is studied. Here, the PID control method is adopted to make the system stable. In order to adjust the PID gains, a three-layer neural network, which is based on the back propagation method, is used. Meanwhile, the time for training the neural network depends on the initial values of PID gains and connection weights. Hence, the genetic algorithm Is considered to shorten the time to find the desired values. Simulation results show the effectiveness of the proposed approach.

• 한국산업융합학회 논문집
• /
• 제22권2호
• /
• pp.87-93
• /
• 2019

This research deals with posture optimization for humanoid robot against external forces using genetic algorithm and neural network. When the robot takes a motion to push an object, the torque of each joint is generated by reaction force at the palm. This study aims to optimize the posture of the humanoid robot that will change this torque. This study finds an optimized posture using a genetic algorithm such that torques are evenly distributed over the all joints. Then, a number of different optimized postures are generated from various the reaction forces at the palm. The data is to be used as training data of MLP(Multi-Layer Perceptron) neural network with BP(Back Propagation) learning algorithm. Humanoid robot can find the optimal posture at different reaction forces in real time using the trained neural network include non-training data.

• 한국정보처리학회논문지
• /
• 제6권5호
• /
• pp.1393-1402
• /
• 1999

본 논문에서는 새로운 학습알고리즘의 3층 전향 신경망을 이용한 입력데이터의 주요 특징추출에 대해서 제안하였다. 제안된 학습알고리즘에서에서는 빠른 수렴속도의 최적화가 가능하도록 하기 위하여 기울기하강의 역전파 알고리즘을 이용하고, 국소최적해를 만났을 때 이를 벗어난 새로운 연결가중치의 설정을 위하여 동적터널링의 역전파 알고리즘을 이용함으로써 빠른 수렴속도로 전역최적해로에 수렴되도록 학습시킬 수 있다. 제안된 학습 알고리즘을 이용한 다층신경망을 $12{\times}12$ 픽셀의 영상 데이터들과 $128{\times}128$ 픽셀의 Lenna 영상데이터를 대상으로 시뮬레이션한 결과, 단층신경망을 이용하는 Sanger 방법이나 측면연결을 가지는 단충신경망을 이용하는 Foldiak 방법 및 기울기하강에 기초를 둔 기존의 역전파 알고리즘을 이용한 다층신경망에 의한 결과와 비교할 때 더욱 우수한 수렴성능과 추출성능이 있음을 확인할 수 있었다.

• 전자공학회논문지CI
• /
• 제37권1호
• /
• pp.21-31
• /
• 2000

본 논문에서는 이산 시간 신경망 제어 시스템의 안정도에 대한 해석을 하도록 한다. 우선 리아프노프의 직접법을 이용하여 신경망제어기를 포함하고 있는 시스템의 안정조건을 체계적으로 유도하고 이 유도된 안정조건을 반영하여 수정된 역전파 알고리즘을 제안한다. 이 수정된 역전파 알고리즘은 유도된 신경망 제어기 시스템의 안정조건을 반영한 학습 규칙이고 따라서 이를 이용하여 학습된 신경망 제어기의 경우 안정성을 보장하게 된다. 끝으로 컴퓨터 모의 실험에서는 제안한 신경망 제어 시스템의 안정조건과 이를 반영한 수정 역전파 알고리즘을 통하여 주어진 플랜트를 학습 제어하도록 한다.

• Smart Structures and Systems
• /
• 제12권3_4호
• /
• pp.345-361
• /
• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

• Nuclear Engineering and Technology
• /
• 제55권6호
• /
• pp.2125-2138
• /
• 2023

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objective functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.

• 전자공학회논문지B
• /
• 제31B권12호
• /
• pp.116-123
• /
• 1994

A modified Hybrid learning rule(MHLR) is proposed, which is derived from combining the Back Propagation algorithm that is known as an excellent classifier with modified Hebbian by changing the orginal Hebbian which is a good feature extractor. The network architecture of MHLR is multi-layered neural network. The weights of MHLR are calculated from sum of the weight of BP and the weight of modified Hebbian between input layer and higgen layer and from the weight of BP between gidden layer and output layer. To evaluate the performance, BP, MHLR and the proposed Hybrid learning rule (HLR) are simulated by Monte Carlo method. As the result, MHLR is the best in recognition rate and HLR is the second. In learning speed, HLR and MHLR are much the same, while BP is relatively slow.

• 제어로봇시스템학회논문지
• /
• 제4권2호
• /
• pp.209-214
• /
• 1998

In this paper, we are dealing with the problem of controlling unknown nonlinear dynamical system by using neural networks. A novel error self-recurrent(ESR) neural model is presented to perform black-box identification. Through the various outcome of the experiment, a new neural network is seen to be considerably faster than the BP algorithm and has advantages of being less affected by poor initial weights and learning rate. These characteristics make it flexible to design the controller in real-time based on neural networks model. In addition, we design an adaptive PID controller that Keyser suggested by using ESR neural networks, and present a method on the implementation of adaptive controller based on neural network for practical applications. We obtained good results in the case of robot manipulator experiment.

• 한국통신학회논문지
• /
• 제21권8호
• /
• pp.2119-2132
• /
• 1996

본 연구에서는 구간 벡터의 비선형 사상의 근사를 행하기 위한 4가지 신경회로망의 학습 알고리즘을 제안한다. 제안된 방법에 있어서, 신경회로망의 학습에 이용되는 입출력 데이터 쌓은 구간으로 구성되어 있다. 첫번째 방법은 전처리된 학습용 데이터 상을 통상의 역전파 알고리즘에 직접 응용하는 것이고, 두번째 방법은 두 개의 역전파 알고리즘을 이용하는 것이다. 세번째 방법은 구간 입출력 데이터를 처리할 수 있는 역전파 알고리즘으로 확장한 것이다. 마지막 방법은 구간 결합강도 및 구간 역치를 가진 신경회로망으로 확장한 것이다. 제안된 이 방법들은 컴퓨터 시뮬레이션에 의해 서로 비교 평가된다.