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检索条件"主题词=Backpropagation Algorithms"

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An Adaptive System Identification Algorithm with a General Performance Index Based on Entropy Optimization

An Adaptive System Identification Algorithm with a General P...

引用

Chinese Control Conference (CCC)

作者： Liu Yan Ren Xuemei Wang Zibin Na Jing Beijing Institute of Technology Beijing China

This paper presents an entropy minimization algorithm for nonlinear system identification based on the information theory. The Parzen windowing estimator is used to approximate the entropy when the probability density functions of the variances can not be known as a priori or the variances are not realistically expressed with the traditional probability density functions. A general performance index based on the information entropy is discussed in this paper. Minimizing the performance index adopted can make the desired output of the adaptive system being tracked directly by the output of the neural network identifier. Furthermore, this performance index can be easily extended when treating other control problems. The performance of the entropy optimal algorithm is shown by several simulations with backpropagation neural networks.

关键词： Adaptive systems System identification Performance analysis Probability density function Neural networks Minimization methods Nonlinear systems Information theory Information entropy backpropagation algorithms

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Face Detection by DSP Using Directly Connected Camera

Face Detection by DSP Using Directly Connected Camera

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International Conference on Radioelektronika

作者： Petr Delong Boris Polikarpov Michal Krumnikl Dept. of Computer Science Technical University of Ostrava Ostrava-Poruba Czech Republic Dept. of Computer Science Technical University of Ostrava 17.listopadu 15 708 33 Ostrava-Poruba Czech Republic michal.krumnikl@vsb.cz

ISBN: (纸本)1424408210

This work deals with face detection with using of directly connected camera to signal processor Blackfin (produced by Analog Devices). This work consists of description of interconnection between monochromatic camera KAC-9618 and DSP processor Blackfin BF-537. An algorithm of face detection and algorithm for communication between camera and DSP are also included. First of them is based on using of backpropagation neural network. Camera is controlled by setting values of its registers via I2C serial bus. In addition, there is an overview of used neural network.

关键词： Face detection Digital signal processing Cameras Neural networks Frequency synchronization Pins Signal processing algorithms Hyperspectral imaging Indexes backpropagation algorithms

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Development and Performance Evaluation of a Neural Signal-based Assistive Computer Interface

Development and Performance Evaluation of a Neural Signal-ba...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Changmok Choi Hyonyoung Han Chunwoo Kim Jung Kim School of Mechanical Aerospace & Systems Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea School of Mechanical and Aerospace Engineering Seoul National University Seoul South Korea

This paper presents the development and performance evaluation of a human-computer interface that enables a limb-disabled person to access a computer via neural signals. For this purpose, electromyogram (EMG) signals were extracted from four muscles on the lower arm, and signal statistics (namely the mean and variance) were used for a filtering process. Six patterns were then classified through the application of a supervised multilayer neural network trained by a backpropagation algorithm. To extract the user's intentions, such as cursor movements and a clicking, the authors applied the neural network in the classification of the six patterns. In addition, an on-screen keyboard was developed so that letters of the Roman and Korean alphabets could be keyed into the computer. Finally, to confirm the appropriation of the developed computer interface, the authors applied Fitts' law in an experimental study to evaluate the performance of the computer interface. The experimental results show that the computer interface had an index of performance, or bandwidth, of 1.299. However, although the developed EMG-based human-computer interface had a lower index of performance than a mouse, it provides an alternative means of computer access for those with a disabled upper limb.

关键词： Computer interfaces Signal processing Multi-layer neural network Neural networks Electromyography Muscles Statistics Filtering Application software backpropagation algorithms

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A New Approach for Function Approximation Based on Adaptive Particle Swarm Optimization

A New Approach for Function Approximation Based on Adaptive ...

引用

International Conference on Natural Computation (ICNC)

作者： Fei Han Qing-Hua Ling School of Computer Science and Telecommunication Engineering Jiangsu University Zhenjiang Jiangsu China School of Electronics and Information Jiangsu University Zhenjiang Jiangsu China

In this paper, a new approach for function approximation problem is proposed to obtain better generalization performance and faster convergent rate. It is well known that gradient-based learning algorithm for feedforward neural networks (FNN) such as backpropagation (BP) algorithm is apt to be trapped in local minima, which leads to worse generalization performance and slower convergence rate. Therefore, in the new approach, first, adaptive particle swarm optimization (APSO) is applied to train network to search global minima. Second, with the trained weights produced by APSO, the network is trained with a constrained learning algorithm (CLA). Moreover, the CLA in the new approach can improve convergence capability by decreasing the input-to-output mapping sensitivity of the network and penalizing the high frequency component in the training data. Due to combined APSO with the CLA, the new approach has better generalization performance and convergence rate. Finally, simulation results are given to verify the efficiency and effectiveness of the proposed learning approach.

关键词： Function approximation Particle swarm optimization Neural networks Convergence Birds Frequency Feedforward neural networks Cost function Training data backpropagation algorithms

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A Real-time EMG-based Assistive Computer Interface for the Upper Limb Disabled

A Real-time EMG-based Assistive Computer Interface for the U...

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IEEE International Conference on Rehabilitation Robotics (ICORR)

作者： Changmok Choi Jung Kim School of Mechanical Aerospace & Systems Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

This paper presents the design of an assistive real-time system for the upper limb disabled to access a computer via residual muscle activities without standard computer interfaces (e.g. a mouse and a keyboard). For this purpose, electromyogram (EMG) signals from muscles in the lower arm were extracted and filtered using signal statistics (mean and variance). In order to control movement and clicking of a cursor from the obtained signals, six patterns were classified, applying a supervised multi-layer neural network trained by a backpropagation algorithm. In addition, an on-screen keyboard was developed, making it possible to enter Roman and Korean letters on the computer. Using this computer interface, the user can browse the Internet and read/send e-mail. The developed computer interface provides an alternative means for individuals with motor disabilities to access computers. A possible extension of our interface methodology can be incorporated in controlling bionic robot systems for the limb disabled (e.g. exoskeletons, limb prostheses).

关键词： Computer interfaces Muscles Keyboards Real time systems Mice Electromyography Statistics Multi-layer neural network backpropagation algorithms Internet

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Evolutionary Neural Networks Applied to Land-cover Classification in Zhaoyuan, China

Evolutionary Neural Networks Applied to Land-cover Classific...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Yan Guo Lishan Kang Fujiang Liu Huashan Sun Linlu Mei School of Computer China University of Geoscience Wuhan China Wuhan University of China Wuhan China China University of Geoscience Wuhan China

This paper proposes a method for the classification of land cover in remote sensing imagery using evolutionary artificial neural networks (EANN) compared against multilayer perceptrons (MLP) with backpropagation algorithm. Evolutionary neural networks have combined the features of artificial neural networks (ANN) and evolutionary algorithms (EA) in the way that simultaneously evolving ANN architecture and weights. The parsimony of evolved ANN is encouraged by preferring node mutation and connection mutation. This enables consistent reductions of mean square errors of spectral classification with respect to sample pixels. Land-cover classification experiments were carried out by EANN-based classifiers and MLP-based classifiers in a 300times300 pixels Landsat-7 Enhanced Thematic Mapper plus (ETM+) high-resolution image of Zhaoyuan in Shandong province in eastern China. We found that the use of evolutionary algorithms for finding the optimal ANN results mainly in improvements in overall accuracy of an ANN with backpropagation algorithm and produce more compact ANN with good generalization ability in comparison with MLP. It is observed that classification accuracy of up to 90% is achievable for Landsat data produced by EANN.

关键词： Neural networks Artificial neural networks Remote sensing backpropagation algorithms Evolutionary computation Genetic mutations Satellites Multilayer perceptrons Mean square error methods Pixel

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Particle Swarm Optimization of Neural Network Architectures andWeights

Particle Swarm Optimization of Neural Network Architectures ...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Marcio Carvalho Teresa B. Ludermir Center of Informatics Federal University of Pernambuco (UFPE) Recife Pernambuco Brazil

The optimization of architecture and weights of feed forward neural networks is a complex task of great importance in problems of supervised learning. In this work we analyze the use of the particle swarm optimization algorithm for the optimization of neural network architectures and weights aiming better generalization performances through the creation of a compromise between low architectural complexity and low training errors. For evaluating these algorithms we apply them to benchmark classification problems of the medical field. The results showed that a PSO-PSO based approach represents a valid alternative to optimize weights and architectures of MLP neural networks.

关键词： Particle swarm optimization Neural networks backpropagation algorithms Genetic algorithms Pattern classification Hybrid intelligent systems Informatics Feeds Feedforward neural networks Supervised learning

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An Intelligent Method of Impedance Measurement Employing PSO-Aided Neuro-Fuzzy System with LMS Algorithm

An Intelligent Method of Impedance Measurement Employing PSO...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Amitava Chatterjee Mita Dutta Anjan Rakshit Electrical Engineering Department Jadavpur University Kolkata India

A sophisticated impedance measurement technique, using an automatic digital ac bridge, is developed which is capable of providing fast and accurate real life measurement. The measurement technique employs LMS algorithm to achieve fast balance in real time. The present paper proposes to employ an intelligent neuro-fuzzy based accuracy improvement module for the LMS bridge. The objective of the neuro-fuzzy system is to add a synthetic phase offset to improve accuracy of the phase measurement in real life. The neuro-fuzzy system is successfully trained by employing particle swarm optimization (PSO), a relatively new combinatorial metaheuristic technique. The success of the proposed technique is effectively demonstrated by employing the bridge in real life for a variety of unknown impedances under measurement.

关键词： Impedance measurement Fuzzy neural networks Least squares approximation Bridge circuits Phase measurement Frequency backpropagation algorithms Phase estimation Artificial neural networks Instruments

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Bio-inspired miniaturized instrument in system-on-chip for robust on-site biomarker recognition

Bio-inspired miniaturized instrument in system-on-chip for r...

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IEEE/NLM Life Science Systems and Applications Workshop

作者： Wai-Chi Fang Jaw-Chyng Lue National Chiao Tung University Hsinchu Taiwan Department of Electrical Engineering-Electrophysics University of Southern California Los Angeles CA USA

A compact integrated system-on-chip (SoC) architecture solution for robust, real-time, and on-site genetic analysis and biomarker recognition has been developed. This microsystem solution is noise-tolerable and suitable for analyzing the weak fluorescence patterns from a PCR prepared dual-labeled DNA microchip assay. In the architecture, a preceding VLSI differential logarithm microchip is designed for effectively computing the logarithm of the normalized input fluorescence signals. A posterior VLSI artificial neural network (ANN) processor chip is used for analyzing the processed signals from the differential logarithm stage. A single-channel logarithmic circuit was fabricated and characterized. A prototype ANN chip with unsupervised winner-take-all (WTA) function was designed, fabricated, and tested. An ANN learning algorithm using a novel sigmoid-logarithmic transfer function based on the supervised backpropagation (BP) algorithm is proposed for robustly recognizing low intensity patterns. Our results show the trained new ANN can recognize low fluorescence patterns better than an ANN using the conventional sigmoid function.

关键词： Instruments System-on-a-chip Biomarkers Artificial neural networks Fluorescence Noise robustness Computer architecture Very large scale integration backpropagation algorithms Pattern recognition

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Image Classification Using Wavelet Coefficients in Low-pass Bands

Image Classification Using Wavelet Coefficients in Low-pass ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Weibao Zou Yan Li Centre for Computing & Data Simulation Shenzhen Institute of Advanced Technology Chinese Academy and Sciences Shenzhen China Department of Mathematics and Computing University of Southern Queensland QLD Australia

In this paper, a method based on wavelet coefficients in low-pass bands is proposed for the image classification with adaptive processing of data structures to organize a large image database. After an image is decomposed by wavelet, its features can be characterized by the distribution of histograms of wavelet coefficients. The coefficients are respectively projected onto x and y directions. For different images, the distribution of histograms of wavelet coefficients in low-pass bands is substantially different. However, the one in high-pass bands is not as different, which makes the performance of classification not reliable. This paper presents a method for image classification based on wavelet coefficients in low-pass bands only. Images are arranged into a tree structure. The nodes can then be represented by the distribution of histograms of these wavelet coefficients. 2940 images derived from seven categories are used for image classification. Based on the wavelet coefficients in low-pass bands, the improvement of classification rate on the training data set is up to 11%, and the improvement of classification rate on the testing data set reaches 20%. Experimental results show that our proposed approach for image classification is more effective and reliable.

关键词： Image classification Wavelet coefficients Neural networks Data structures Histograms Tree data structures Image databases Image processing backpropagation algorithms Optimization methods

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