• Title/Summary/Keyword: back propagation algorithm

Search Result 896, Processing Time 0.028 seconds

A study on nonlinear data-based modeling using fuzzy neural networks (퍼지신경망을 이용한 비선형 데이터 모델링에 관한 연구)

  • Kwon, Oh-Gook;Jang, Wook;Joo, Young-Hoon;Choi, Yoon-Ho;Park, Jin-Bae
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1997.10a
    • /
    • pp.120-123
    • /
    • 1997
  • This paper presents models of fuzzy inference systems that can be built from a set of input-output training data pairs through hybrid structure-parameter learning. Fuzzy inference systems has the difficulty of parameter learning. Here we develop a coding format to determine a fuzzy neural network(FNN) model by chromosome in a genetic algorithm(GA) and present systematic approach to identify the parameters and structure of FNN. The proposed FNN can automatically identify the fuzzy rules and tune the membership functions by modifying the connection weights of the networks using the GA and the back-propagation learning algorithm. In order to show effectiveness of it we simulate and compare with conventional methods.

  • PDF

Classification System of EEG Signals During Mental Tasks

  • Seo Hee Don;Kim Min Soo;Eoh Soo Hae;Huang Xiyue;Rajanna K.
    • Proceedings of the IEEK Conference
    • /
    • 2004.08c
    • /
    • pp.671-674
    • /
    • 2004
  • We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

  • PDF

Speeding-up for error back-propagation algorithm using micro-genetic algorithms (미소-유전 알고리듬을 이용한 오류 역전파 알고리듬의 학습 속도 개선 방법)

  • 강경운;최영길;심귀보;전홍태
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1993.10a
    • /
    • pp.853-858
    • /
    • 1993
  • The error back-propagation(BP) algorithm is widely used for finding optimum weights of multi-layer neural networks. However, the critical drawback of the BP algorithm is its slow convergence of error. The major reason for this slow convergence is the premature saturation which is a phenomenon that the error of a neural network stays almost constant for some period time during learning. An inappropriate selections of initial weights cause each neuron to be trapped in the premature saturation state, which brings in slow convergence speed of the multi-layer neural network. In this paper, to overcome the above problem, Micro-Genetic algorithms(.mu.-GAs) which can allow to find the near-optimal values, are used to select the proper weights and slopes of activation function of neurons. The effectiveness of the proposed algorithms will be demonstrated by some computer simulations of two d.o.f planar robot manipulator.

  • PDF

A classification techiniques of J-lead solder joint using neural network (신경 회로망을 이용한 J-리드 납땜 상태 분류)

  • Yu, Chang-Mok;Lee, Joong-Ho;Cha, Young-Yeup
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.5 no.8
    • /
    • pp.995-1000
    • /
    • 1999
  • This paper presents a optic system and a visual inspection algorithm looking for solder joint defects of J-lead chip which are more integrate and smaller than ones with Gull-wing on PCBs(Printed Circuit Boards). The visual inspection system is composed of three sections : host PC, imaging and driving parts. The host PC part controls the inspection devices and executes the inspection algorithm. The imaging part acquires and processes image data. And the driving part controls XY-table for automatic inspection. In this paper, the most important five features are extracted from input images to categorize four classes of solder joint defects in the case of J-lead chip and utilized to a back-propagation network for classification. Consequently, good accuracy of classification performance and effectiveness of chosen five features are examined by experiment using proposed inspection algorithm.

  • PDF

Self-learning control of nonlinear system using Back-propagation neural networks. (Back-Propagation 신경 회로망을 이용한 비선형 시스템의 자기 학습 제어)

  • Park, C.H.;Song, H.S.;Lee, J.T.;Park, Y.S.
    • Proceedings of the KIEE Conference
    • /
    • 1992.07a
    • /
    • pp.231-235
    • /
    • 1992
  • A new algorithm is proposed to identify the structure and the parameters of the nonlinear discrete-time plant with only the unknown dynamics and the weak informations about its structure. The proposed algorithm is constructed with the compensation method of weghing values using its previous derivatives and with the efficient technique updating self-learning coefficients. The result in this application is thought to prove the effectiveness of the algorithm proposed in this paper and its superiority to the conventional ones.

  • PDF

Face Recognition Using Knowledge-Based Feature Extraction and Back-Propagation Algorithm (지식에 기초한 특정추출과 역전파 알고리즘에 의한 얼굴인식)

  • 이상영;함영국;박래홍
    • Journal of the Korean Institute of Telematics and Electronics B
    • /
    • v.31B no.7
    • /
    • pp.119-128
    • /
    • 1994
  • In this paper, we propose a method for facial feature extraction and recognition algorithm using neural networks. First we extract a face part from the background image based on the knowledge that it is located in the center of an input image and that the background is homogeneous. Then using vertical and horizontal projections. We extract features from the separated face image using knowledge base of human faces. In the recognition step we use the back propagation algorithm of the neural networks and in the learning step to reduce the computation time we vary learning and momentum rates. Our technique recognizes 6 women and 14 men correctly.

  • PDF

Adaptive Control of Non-linear Dynamic System using Neural Network (신경 회로망을 이용한 비선형 동적 시스템의 적응 제어)

  • Jang, Seong-Whan;Cho, Hyeon-Seob;Kim, Ki-Cheol;Choi, Bong-Shik;Yu, In-Ho
    • Proceedings of the KIEE Conference
    • /
    • 1995.07b
    • /
    • pp.953-955
    • /
    • 1995
  • Studied on identification of nonlinear system with unknown variables and adaptive control were successful. We need a mathmatical model when control a dynamic system using adaptive control technique, but it is very difficult due to its nonlinearity. In this paper, we described about performance improvement of error back-propagation algorithm and learning algorithm of non-linear dynamic system. We examined the proposed back-propagation learn algorithm for through an experiment.

  • PDF

A Classification Techniques of Solder Joint Using Neural Network in Visual Inspection System (시각 검사 시스템에서 신경 회로망을 이용한 납땜 상태 분류 기법)

  • 오제휘;차영엽
    • Journal of the Korean Society for Precision Engineering
    • /
    • v.15 no.7
    • /
    • pp.26-35
    • /
    • 1998
  • This paper presents a visual inspection algorithm looking for solder joint defects of IC chips on PCBs (Printed Circuit Boards). In this algorithm, seven features are proposed in order to categorize the solder joints into four classes such as normal, insufficient, excess, and no solder, and optimal back-propagation network is determined by error evaluation which depend on the number of neurons in hidden and out-put layers and selection of the features. In the end, a good accuracy of classification performance, an optimal determination of network structure and the effectiveness of chosen seven features are examined by experiment using proposed inspection algorithm.

  • PDF

The Efficient Edge Detection using Genetic Algorithms and Back-Propagation Network (유전자와 역전파 알고리즘을 이용한 효율적인 윤곽선 추출)

  • Park, Chan-Lan;Lee, Woong-Ki
    • The Transactions of the Korea Information Processing Society
    • /
    • v.5 no.11
    • /
    • pp.3010-3023
    • /
    • 1998
  • GA has a fast convergence speed in searching the one point around optimal value. But it's convergence time increase in searching the region around optimal value because it has no regional searching mechanism. BP has the tendency to converge the local minimum because it has global searching mechanism. To overcome these problems, a method in which a genetic algorithm and a back propagation are applied in turn is proposed in this paper. By using a genetic algorithm, we compute optimal synaptic strength and offset value. And then, these values are fed to the input of the back propagation. This proposed method is superior to each above method in improving the convergence speed.

  • PDF

Control Method using Neural Network of Hybrid Learning Rule (혼합형 학습규칙 신경 회로망을 이용한 제어 방식)

  • 임중규;이현관;권성훈;엄기환
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • 1999.05a
    • /
    • pp.370-374
    • /
    • 1999
  • The proposed algorithm used the Hybrid teaming rule in the input and hidden layer, and Back-Propagation teaming rule in the hidden and output layer. From the results of simulation of tracking control with one link manipulator as a plant, we verify the usefulness of the proposed control method to compare with common direct adaptive neural network control method; proposed hybrid teaming rule showed faster loaming time faster settling time than the direct adaptive neural network using Back-propagation algorithm. Usefulness of the proposed control method is that it is faster the learning time and settling time than common direct adaptive neural network control method.

  • PDF