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检索条件"主题词=Backpropagation algorithms"

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A Normalization Method Based on Rough Set Theory

A Normalization Method Based on Rough Set Theory

引用

International Workshop on Intelligent Systems and Applications, ISA

作者： Chu Jian Department of Automation Engineering Tianjin University of Technology and Education Tianjin China

The training time of classifier based on neural network is very long using the conventional normalization when the distances between samples of different classes are too small. To overcome the disadvantage, the normalization method based on rough set theory is proposed. By normalizing samples using rough ser theory, the samples which are near but belong to different classes are taken apart. The normalized samples are used to train neural network. The method is applied into neural network based fault line detection for distribution network. The simulation results show that the training time of neural network with processed samples is shorter markedly.

关键词： Set theory Neural networks Information systems Fault detection Feedforward neural networks backpropagation algorithms Automation Educational technology Object detection Uncertainty

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Controller for TORCS created by imitation

Controller for TORCS created by imitation

引用

IEEE Symposium on Computational Intelligence and Games, CIG

作者： Jorge Munoz German Gutierrez Araceli Sanchis Computer Science Department Universidad Carlos III de Madrid Leganes Spain

This paper is an initial approach to create a controller for the game TORCS by learning how another controller or humans play the game. We used data obtained from two controllers and from one human player. The first controller is the winner of the WCCI 2008 Simulated Car Racing Competition, and the second one is a hand coded controller that performs a complete lap in all tracks. First, each kind of controller is imitated separately, then a mix of the data is used to create new controllers. The imitation is performed by means of training a feed forward neural network with the data, using the backpropagation algorithm for learning.

关键词： Humans Games Artificial intelligence Computational modeling Computer simulation Artificial neural networks backpropagation algorithms Internet Feeds Neural networks

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Robust Conicoid Fitting in Converting GPS Height

Robust Conicoid Fitting in Converting GPS Height

引用

International Conference on Information Engineering and Computer Science (ICIECS)

作者： Weiduo Huang Dept. of Hydraulic Eng. Zhejiang Water Conservancy & Hydropower Coll. Hangzhou China

Height difference between GPS height and normal height is called height anomaly. If height anomaly for a point can be estimated, GPS height above the surface of the WGS-84 ellipsoid can be converted into normal height, which is used in engineering applications. A new robust procedure by using the conicoid fitting method (CFM) with the Huber M-estimation was proposed, to convert GPS height to normal height using some survey marks of a GPS control net where both normal height and GPS height are known. The robust CFM was presented on the example of a B-order GPS control net. The accuracy was analyzed and the results are promising. The impact of survey marks distribution and density on fitting accuracy was also discussed. In flat areas, converting GPS height to normal height by using CFM arrives at sub-centimeter level accuracy under some conditions.

关键词： Robustness Global Positioning System Surface fitting Sea surface Ellipsoids Robust control Mathematical model backpropagation algorithms Testing Water conservation

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Hebbian learning with winner take all for spiking neural networks

Hebbian learning with winner take all for spiking neural net...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ankur Gupta Lyle N. Long Department of Computer Science and Engineering Pennsylvania State University University Park PA USA Aerospace Engineering Bioengineering and Mathematics Pennsylvania State University University Park PA USA

Learning methods for spiking neural networks are not as well developed as the traditional rate based networks, which widely use the back-propagation learning algorithm. We propose and implement an efficient Hebbian learning method with homeostasis for a network of spiking neurons. Similar to STDP, timing between spikes is used for synaptic modification. Homeostasis ensures that the synaptic weights are bounded and the learning is stable. The winner take all mechanism is also implemented to promote competitive learning among output neurons. We have implemented this method in a C++ object oriented code (called CSpike). We have tested the code on four images of Gabor filters and found bell-shaped tuning curves using 36 test set images of Gabor filters in different orientations. These bell-shapes curves are similar to those experimentally observed in the V1 and MT/V5 area of the mammalian brain.

关键词： Hebbian theory Neural networks Neurons Biological neural networks Object oriented modeling Biological information theory backpropagation algorithms Learning systems Artificial neural networks Timing

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A method of offset error reduction of simple adaptive control using neural networks for MIMO nonlinear systems

A method of offset error reduction of simple adaptive contro...

引用

SICE Annual Conference

作者： Muhammad Yasser Ikuro Mizumoto IDS Research Group Hiroshima Japan Department of Intelligent Mechanical Systems Kumamoto University Kumamoto Japan

This paper proposes an SAC using neural networks with offset error reduction for MIMO nonlinear systems. In this proposed method, the control input for the nonlinear plant is given by the sum of the output of a simple adaptive controller and the output of neural networks. The role of neural networks is to compensate for constructing a linearized model so as to minimize the output error caused by nonlinearities in the control system. The neural networks use the backpropagation algorithm for the learning process. The role of simple adaptive controller is to perform the model matching for the linear system with unknown structures to a given linear reference model. In this method, only part of the control input is fed to the PFC. Thus, the proposed method will reduce the offset error, and both of the augmented plant output and the real plant output can follow significantly close to the output of the reference model. Finally, the effectiveness of this method is confirmed through computer simulations.

关键词： Error correction Adaptive control Neural networks MIMO Nonlinear systems Programmable control Control system synthesis Nonlinear control systems Control nonlinearities backpropagation algorithms

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Semi-supervised clustering using similarity neural networks

Semi-supervised clustering using similarity neural networks

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Stefano Melacci Marco Maggini Lorenzo Sarti Department of Information Engineering University of Sienna Siena Italy

Similarity neural networks (SNNs) are a novel neural network model designed to learn similarity measures for pairs of patterns, exploiting binary supervision. SNNs guarantee to compute non negative and symmetric measures, and show good generalization capabilities even if a small set of supervised pairs is used for training. The application of the new model to K-Means like semi-supervised clustering is investigated, introducing a technique that allows the algorithm to compute cluster centroids by means of backpropagation on the input layer of the SNN, biased by a regularization function. The experiments carried out on some datasets from the UCI repository show that SNN based clustering almost always outperforms other methods proposed in the literature.

关键词： Neural networks Clustering algorithms backpropagation algorithms Partitioning algorithms Iterative algorithms Multi-layer neural network Computer architecture Particle measurements Extraterrestrial measurements Iterative methods

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Neuro-Calibration of a Camera Using Particle Swarm Optimization

Neuro-Calibration of a Camera Using Particle Swarm Optimizat...

引用

International Conference on Emerging Trends in Engineering and Technology, ICETET

作者： Sanjeev Kumar Balasubramanian Raman Jonathan Wu Department of Maths & Computer Science University of Udine Udine Italy Department of Mathematics Indian Institute of Technology Roorkee Roorkee India Department of Electrical and Computer Eng University of Windsor Windsor ONT Canada

In this paper, a particle swarm optimization (PSO) based camera calibration approach is presented to determine the external and internal calibration parameters from the knowledge of a given set of points in object space. First, the image formation model for a pinhole camera is formulated in terms of a feed-forward neural network (NN) and then this neural network is trained using particle swarm optimization. The effect of noise and number of control points are studied in the estimation of calibration parameters. Results from our extensive study are presented to demonstrate the excellent performance of the proposed technique in terms of convergence, accuracy, and robustness.

关键词： Cameras Particle swarm optimization Calibration Neural networks Robot vision systems Genetic algorithms Artificial neural networks backpropagation algorithms Feedforward neural networks Image reconstruction

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Classification Technology for Automatic Surface Defects Detection of Steel Strip Based on Improved BP Algorithm

Classification Technology for Automatic Surface Defects Dete...

引用

International Conference on Natural Computation (ICNC)

作者： Kaixiang Peng Xuli Zhang University of Science and Technology Beijing Beijing Beijing CN

The quality detection of the cold-strip steel using artificial neural networks is studied. A simple Back-propagation (BP) algorithm based on error function was presented. It deals with the saturation areas that play a significant role in the slow convergence of standard BP algorithm. A modified error function was constructed to make the weight adjustment to avoid falling into the saturation areas. The simulation and experiment results show the effect of improved BP algorithm on the classification of the surface defects of steel strip.

关键词： Steel Strips Neural networks backpropagation algorithms Convergence Metals industry Computer networks Artificial neural networks Computer errors Inspection

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Robust Supplementary Controllers for AVR and PSS

Robust Supplementary Controllers for AVR and PSS

引用

IEEE India Conference (INDICON)

作者： M. Uma Vani G. S. Raju K. R. L. Prasad EEE Department LBRCE Mylavaram India EEE Department VNR VJIT Hyderabad India

ISBN: (纸本)9781424448586;9781424448593

The paper presents a step-by-step design methodology of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and H ¿ optimization methods based Automatic Voltage Regulator (AVR) and Power System Stabilizer (PSS).This paper demonstrates their performance in a single-machine-infinite-bus power system through digital simulation. The ANFIS design employs a zero and a first order Sugeno fuzzy model, whose parameters are tuned off-line through hybrid learning algorithm. This algorithm is a combination of Least Square Estimator and Error backpropagation method. The performance of the ANFIS and H ¿ optimization methods based AVR and PSS in damping oscillation is then compared with conventional AVR and PSS performance. It is found that the damping characteristics of both ANFIS and optimization based H ¿ AVR and PSS are better than the conventional AVR and PSS. The effectiveness of the proposed ANFIS-based AVR and PSS in small-signal stability is thus established. Index Temts- AVR PSS, Fuzzy Logic, Adaptive NeuroFuzzy Inference System, H ¿ optimization, SugenoFuzzy Model, Hybrid Learning Algorithm.

关键词： Robust control Hybrid power systems Power system modeling Adaptive systems Optimization methods Power system simulation backpropagation algorithms Damping Automatic control Design methodology

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Estimation of mixing ratios of two mixture solutions using shear horizontal surface acoustic wave (SH-SAW) sensors

Estimation of mixing ratios of two mixture solutions using s...

引用

National Conference on Radio Science (NSRC)

作者： Taha El-Sayed Taha Mohmed A.A. Eldosoky Ahmed Mostafa El-Sayed Communication Department Faculty of Electronics Engineering Menoufia University Egypt Department of Biomedical Eng Faculty of Engineering Helwan university Egypt

Recently, surface acoustic waves sensors are developed and used in many applications. Estimation of the mixed electrolytes is one of these applications. This is done by using a flow of this solution passed between two interdigital transducers of SAW sensor. A certain kind of SAW will be generated and called shear horizontal (SH-SAW). This wave is very sensitive to these solutions, where clear perturbations in the attenuation and the frequency shifts of the received signal are observed. Experimental results of the flow liquid system with SH-SAW sensor are taken. From this data, an algorithm is proposed with the aid of backpropagation neural network that will be used directly to identify the two components of ions and determine the mixing ratio between the electrolytes. The durations of the time responses, fast Fourier transform (FFT), discrete cosine transform (DCT) are used as features in this algorithm.

关键词： Surface acoustic waves Acoustic waves Acoustic sensors backpropagation algorithms Discrete cosine transforms Acoustic transducers Attenuation Frequency Fluid flow Sensor systems

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