• Title/Summary/Keyword: Multilayer Neural Network

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New criteria to fix number of hidden neurons in multilayer perceptron networks for wind speed prediction

  • Sheela, K. Gnana;Deepa, S.N.
    • Wind and Structures
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    • v.18 no.6
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    • pp.619-631
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    • 2014
  • This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.

A study on Forecasting The Operational Continuous Ability in Battalion Defensive Operations using Artificial Neural Network (인공신경망을 이용한 대대전투간 작전지속능력 예측)

  • Shim, Hong-Gi;Kim, Sheung-Kown
    • Journal of Intelligence and Information Systems
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    • v.14 no.3
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    • pp.25-39
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    • 2008
  • The objective of this study is to forecast the operational continuous ability using Artificial Neural Networks in battalion defensive operation for the commander decision making support. The forecasting of the combat result is one of the most complex issue in military science. However, it is difficult to formulate a mathematical model to evaluate the combat power of a battalion in defensive operation since there are so many parameters and high temporal and spatial variability among variables. So in this study, we used company combat power level data in Battalion Command in Battle Training as input data and used Feed-Forward Multilayer Perceptrons(MLP) and General Regression Neural Network (GRNN) to evaluate operational continuous ability. The results show 82.62%, 85.48% of forecasting ability in spite of non-linear interactions among variables. We think that GRNN is a suitable technique for real-time commander's decision making and evaluation of the commitment priority of troops in reserve.

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Fuzzy Supervised Learning Algorithm by using Self-generation (Self-generation을 이용한 퍼지 지도 학습 알고리즘)

  • 김광백
    • Journal of Korea Multimedia Society
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    • v.6 no.7
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    • pp.1312-1320
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    • 2003
  • In this paper, we consider a multilayer neural network, with a single hidden layer. Error backpropagation learning method used widely in multilayer neural networks has a possibility of local minima due to the inadequate weights and the insufficient number of hidden nodes. So we propose a fuzzy supervised learning algorithm by using self-generation that self-generates hidden nodes by the compound fuzzy single layer perceptron and modified ART1. From the input layer to hidden layer, a modified ART1 is used to produce nodes. And winner take-all method is adopted to the connection weight adaptation, so that a stored pattern for some pattern gets updated. The proposed method has applied to the student identification card images. In simulation results, the proposed method reduces a possibility of local minima and improves learning speed and paralysis than the conventional error backpropagation learning algorithm.

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Multilayer Neural Network Using Delta Rule: Recognitron III (텔타규칙을 이용한 다단계 신경회로망 컴퓨터:Recognitron III)

  • 김춘석;박충규;이기한;황희영
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.40 no.2
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    • pp.224-233
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    • 1991
  • The multilayer expanson of single layer NN (Neural Network) was needed to solve the linear seperability problem as shown by the classic example using the XOR function. The EBP (Error Back Propagation ) learning rule is often used in multilayer Neural Networks, but it is not without its faults: 1)D.Rimmelhart expanded the Delta Rule but there is a problem in obtaining Ca from the linear combination of the Weight matrix N between the hidden layer and the output layer and H, wich is the result of another linear combination between the input pattern and the Weight matrix M between the input layer and the hidden layer. 2) Even if using the difference between Ca and Da to adjust the values of the Weight matrix N between the hidden layer and the output layer may be valid is correct, but using the same value to adjust the Weight matrixd M between the input layer and the hidden layer is wrong. Recognitron III was proposed to solve these faults. According to simulation results, since Recognitron III does not learn the three layer NN itself, but divides it into several single layer NNs and learns these with learning patterns, the learning time is 32.5 to 72.2 time faster than EBP NN one. The number of patterns learned in a EBP NN with n input and output cells and n+1 hidden cells are 2**n, but n in Recognitron III of the same size. [5] In the case of pattern generalization, however, EBP NN is less than Recognitron III.

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Wavelet Neural Network Based Generalized Predictive Control of Chaotic Systems Using EKF Training Algorithm

  • Kim, Kyung-Ju;Park, Jin-Bae;Choi, Yoon-Ho
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.2521-2525
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    • 2005
  • In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

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Neural-network based Computerized Emotion Analysis using Multiple Biological Signals (다중 생체신호를 이용한 신경망 기반 전산화 감정해석)

  • Lee, Jee-Eun;Kim, Byeong-Nam;Yoo, Sun-Kook
    • Science of Emotion and Sensibility
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    • v.20 no.2
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    • pp.161-170
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    • 2017
  • Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

Self-organized Distributed Networks for Precise Modelling of a System (시스템의 정밀 모델링을 위한 자율분산 신경망)

  • Kim, Hyong-Suk;Choi, Jong-Soo;Kim, Sung-Joong
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.11
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    • pp.151-162
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    • 1994
  • A new neural network structure called Self-organized Distributed Networks (SODN) is proposed for developing the neural network-based multidimensional system models. The learning with the proposed networks is fast and precise. Such properties are caused from the local learning mechanism. The structure of the networks is combination of dual networks such as self-organized networks and multilayered local networks. Each local networks learns only data in a sub-region. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the proposed networks. The simulation results of the proposed networks show better performance than the standard multilayer neural networks and the Radial Basis function(RBF) networks.

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Performance Comparision of Multilayer Perceptron Nueral Network and Maximum Likelihood Classifier for Category Classification (카테고리분류를 위한 다층퍼셉트론 신경회로망과 최대유사법의 성능비교)

  • Lim, Tae-Hun;Seo, Yong-Su
    • Journal of Korean Society for Geospatial Information Science
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    • v.4 no.2 s.8
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    • pp.137-147
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    • 1996
  • In this paper, the performances between maximum likelihood classifier based on statistical classification and multilayer perceptrons based on neural network approaches were compared and evaluated Experimental results from both neural network method and statistical method are presented. In addition, the nature of two different approches are analyzed based on the experiments.

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Neural Network Based Camera Calibration and 2-D Range Finding (신경회로망을 이용한 카메라 교정과 2차원 거리 측정에 관한 연구)

  • 정우태;고국원;조형석
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1994.10a
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    • pp.510-514
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    • 1994
  • This paper deals with an application of neural network to camera calibration with wide angle lens and 2-D range finding. Wide angle lens has an advantage of having wide view angles for mobile environment recognition ans robot eye in hand system. But, it has severe radial distortion. Multilayer neural network is used for the calibration of the camera considering lens distortion, and is trained it by error back-propagation method. MLP can map between camera image plane and plane the made by structured light. In experiments, Calibration of camers was executed with calibration chart which was printed by using laser printer with 300 d.p.i. resolution. High distortion lens, COSMICAR 4.2mm, was used to see whether the neural network could effectively calibrate camera distortion. 2-D range of several objects well be measured with laser range finding system composed of camera, frame grabber and laser structured light. The performance of 3-D range finding system was evaluated through experiments and analysis of the results.

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A Robust PID Control Method with Neural Network

  • Kang, Seong-Ho;Lee, Yong-Gu;Eom, Ki-Hwan
    • Journal of information and communication convergence engineering
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    • v.2 no.1
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    • pp.46-51
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    • 2004
  • The problem of reducing the effect of an unknown disturbance on a dynamical system is one of the most fundamental issues in control design. We propose a robust PID (Proportional Integral Derivative) control method with neural network for improving the performance due to the rejection of an unknown disturbance. The proposed system consists of a model of the plant, a conventional PID controller and a multi-layer neural network, and is composed of two loop; the first loop enables the system to achieve stability of system, the second loop rejects an unknown disturbance. Simulation and experiment results show that the proposed method improves considerably on the performance of the conventional PID control method and the typical IMC method using neural network.