• Title/Summary/Keyword: Backpropagation Neural Networks

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Improve Digit Recognition Capability of Backpropagation Neural Networks by Enhancing Image Preprocessing Technique

  • Feng, Xiongfeng;Kubik, K.Bogunia
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.49.4-49
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    • 2001
  • Digit recognition based on backpropagation neural networks, as an important application of pattern recognition, was attracted much attention. Although it has the advantages of parallel calculation, high error-tolerance, and learning capability, better recognition effects can only be achieved with some specific fixed format input of the digit image. Therefore, digit image preprocessing ability directly affects the accuracy of recognition. Here using Matlab software, the digit image was enhanced by resizing and neutral-rotating the extracted digit image, which improved the digit recognition capability of the backpropagation neural network under practical conditions. This method may also be helpful for recognition of other patterns with backpropagation neural networks.

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Using Classification function to integrate Discriminant Analysis, Logistic Regression and Backpropagation Neural Networks for Interest Rates Forecasting

  • Oh, Kyong-Joo;Ingoo Han
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2000.11a
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    • pp.417-426
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    • 2000
  • This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

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Trip Generation Model Using Backpropagation Neural Networks in Comparison with linear/nonlinear Regression Analysis (신경망 이론을 이용한 통행발생 모형연구 (선형/비선형 회귀모형과의 비교))

  • 장수은;김대현;임강원
    • Journal of Korean Society of Transportation
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    • v.18 no.4
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    • pp.95-105
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    • 2000
  • The Purpose of this study is to present a new Trip Generation Model using Backpropagation Neural Networks. For this purpose, it is compared the performance between existing linear/nonlinear Regression models and a new TriP Generation model using Neural Networks. The study was performed according to the below. First, it is analyzed the limits of conventional Regression models, next Proved the superiority of Neural Networks model in theoretical and empirical aspects, and lastly Presented a new approach of Trip Generation methodology. The results show that Backpropagation Neural Networks model is predominant in estimation and Prediction comparable to Regression analysis. Such results mean the possibility of Neural Networks\` application in Trip Generation modeling. Specially under the circumstances of the chancing transportation situations and unstable transportation on vironments, its application in transportation fields will be extended.

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Recognition of vehicle number plate using multi backpropagation neural network (다중 역전파 신경망을 이용한 차량 번호판의 인식)

  • 최재호;조범준
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.22 no.11
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    • pp.2432-2438
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    • 1997
  • This paper proposes recognition system using multi-backpropagation neural networks rather than single backpropagation neural network to enhance the rate of character recognition resultsing from extracting the region of velhicle number in that the image of vehicle number plate from CCD camera has a distinguish feature, that is, illumination of a pattern. The experiment in this paper shows an output that the method using multi-backpropagation neural networks rather than signal backpropagation neural network takes less training time for computation and also has higher recognition rage of vehicle number.

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A new learning algorithm for multilayer neural networks (새로운 다층 신경망 학습 알고리즘)

  • 고진욱;이철희
    • Proceedings of the IEEK Conference
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    • 1998.10a
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    • pp.1285-1288
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    • 1998
  • In this paper, we propose a new learning algorithm for multilayer neural networks. In the error backpropagation that is widely used for training multilayer neural networks, weights are adjusted to reduce the error function that is sum of squared error for all the neurons in the output layer of the network. In the proposed learning algorithm, we consider each output of the output layer as a function of weights and adjust the weights directly so that the output neurons produce the desired outputs. Experiments show that the proposed algorithm outperforms the backpropagation learning algorithm.

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A Study on the Prediction of the Loaded Location of the Composite Laminated Shell by Using Neural Networks (신경회로망을 이용한 복합재료 원통쉘의 하중특성 추론에 관한 연구)

  • 명창문;이영신;류충현
    • Composites Research
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    • v.14 no.5
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    • pp.26-37
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    • 2001
  • After impact analysis of the composite cylindrical shells was performed. obtained outputs at 9 equally divided points of the shell were used as input patterns of the neural networks. Identification of impact loading characteristics was predicted simultaneously. Momentum backpropagation algorithm of neural networks which can modify the momentum coefficient and learning rate was developed and applied to identify the loading characteristics. Hidden layers of the backpropagation increased from 1 layer to 3 layers and trained the loading characteristics. Developed program with variable learning rate was converged close to real load characteristics under 1% error. Inverse engineering which identify the impact loading characteristics can be applicable to the composite laminated cylindrical shells with developed neural networks.

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Using Structural Changes to support the Neural Networks based on Data Mining Classifiers: Application to the U.S. Treasury bill rates

  • Oh, Kyong-Joo
    • 한국데이터정보과학회:학술대회논문집
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    • 2003.10a
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    • pp.57-72
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    • 2003
  • This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

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Identification of Composite Cylindricall shells by Using Neural Networks (신경회로망을 이용한 원통셀의 충격하중 추론에 관한 연구)

  • 명창문;이영신
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.11 no.9
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    • pp.475-485
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    • 2001
  • A study on the structural analysis of the composite laminated cylindrical shell which has simply supported boundary conditions at both ends, was performed. The results were used into the neural networks. Neural networks identify the load characteristics of the composite shells. Momentum Backpropagation which the learning rate can be varied was developed. Input patterns consist of strains at 9 side points which is divided equally. Output layers are the load characteristics. Developed program was used for the training. The training with variable learning rate was converged close to real oad characteristics. Inverse engineering can be applicable to the composite laminated cylindrical shells with developed neural networks.

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A study on the Adaptive Neural Controller with Chaotic Neural Networks (카오틱 신경망을 이용한 적응제어에 관한 연구)

  • Sang Hee Kim;Won Woo Park;Hee Wook Ahn
    • Journal of the Institute of Convergence Signal Processing
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    • v.4 no.3
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    • pp.41-48
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    • 2003
  • This paper presents an indirect adaptive neuro controller using modified chaotic neural networks(MCNN) for nonlinear dynamic system. A modified chaotic neural networks model is presented for simplifying the traditional chaotic neural networks and enforcing dynamic characteristics. A new Dynamic Backpropagation learning method is also developed. The proposed MCNN paradigm is applied to the system identification of a MIMO system and the indirect adaptive neuro controller. The simulation results show good performances, since the MCNN has robust adaptability to nonlinear dynamic system.

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Improved Learning Algorithm with Variable Activating Functions

  • Pak, Ro-Jin
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.815-821
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    • 2005
  • Among the various artificial neural networks the backpropagation network (BPN) has become a standard one. One of the components in a neural network is an activating function or a transfer function of which a representative function is a sigmoid. We have discovered that by updating the slope parameter of a sigmoid function simultaneous with the weights could improve performance of a BPN.

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