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检索条件"主题词=Backpropagation Algorithms"

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A computational intelligence system for cell classification

A computational intelligence system for cell classification

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Wei Lin Jianhong Xiao E. Micheli-Tzanakou Department of Biomedical Engineering Rutgers University Piscataway NJ USA

Artificial neural networks were used to classify blood cells. Compared with existing methods, neural networks are more accurate, efficient, adaptable and information-rich. The implementation of the system in a PC/Windows NT environment using image processing technology and database management allows for a variety of features to be extracted and a variety of training algorithms to be used. In this preliminary study, blood cell images are segmented to individual cells. Features for individual cells, including size, color content and shape related moments, are extracted and used as inputs to a multilayer neural network. backpropagation and ALOPEX training algorithms were used to train the neural network. After less than 2000 training iterations using 95 training sets, the system recognized three kinds of blood cell in a correctness percentage of 100%. This module provides a platform to build a more sophisticated computational intelligent system for cell classification for clinical use.

关键词： Computational intelligence Cells (biology) Blood Artificial neural networks Management training backpropagation algorithms Multi-layer neural network Neural networks Image processing Image databases

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Bankruptcy prediction using connectionist and symbolic learning algorithms

Bankruptcy prediction using connectionist and symbolic learn...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： E. Martineli H. Diniz A.C.P.L.F. De Carvalho S.O. Rezende Department of Computer Science Laboratory of Computational Intelligence-LABIC University of Sao Paulo Sao Paulo Brazil

This article describes the use of connectionist and symbolic learning algorithms in the problem of bankruptcy prediction. Data about Brazilian banks represented by 26 or 10 indicators of their current financial situation were used. The difference among the number of existent examples in the classes of bankrupt and nonbankrupt banks was livened up through the reduction of learning examples of the class of nonbankrupts and the addition of noise samples in the class of bankrupts.

关键词： backpropagation algorithms Economic forecasting Artificial intelligence Artificial neural networks Machine learning algorithms Noise reduction Production Computer science Laboratories Computational intelligence

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Texture characterization using 2D cumulant-based lattice adaptive filtering

Texture characterization using 2D cumulant-based lattice ada...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： M. Sayadi V. Buzenac-Settineri M. Najim ESSTT Tunis Tunisia LESTER-UBS Lorient France Equipe Signal & Image ENSERB Talence France

We take into account the non-Gaussian properties of textures and we propose a new approach for their characterization based on bidimensional adaptive modeling using higher order statistics. The 2D-OLRIV (bidimensional overdetermined lattice recursive instrumental variable) algorithm allows accurate texture model estimation. Sets of 2D-AR coefficients obtained from the 2D reflection coefficients of the lattice model are used to characterize the texture model. This algorithm has the advantage of yielding non-biased estimates of the 2D-AR model even when the texture image is disturbed by Gaussian noise. A multilayer neural network deals with these coefficients in order to classify different textures. In order to evaluate the performance of this approach, the classification sensitivity is evaluated on a set of eight different textures. This characterization approach gives very promising results.

关键词： Lattices Adaptive filters Multi-layer neural network Gaussian noise backpropagation algorithms Instruments Neural networks Equations Adaptive algorithm Resonance light scattering

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Training recurrent neural networks with leap-frog

Training recurrent neural networks with leap-frog

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： J.E.W. Holm N.J.H. Kotze Department of Electrical and Electronic Engineering University of Pretoria Pretoria South Africa

Nonlinear recurrent neural networks are used as theoretical models to model and predict behavior of nonlinear systems such as electrical and mechanical loads and systems. Recurrent networks implement dynamic system models more efficiently than their feedforward counterparts. The efficiency of recurrent nets stems from the infinite number of state and state space trajectories that are exploited to enhance the storage capacity with respect to the weight space dimensionality. Training of recurrent networks is achieved by applying a classical optimization algorithm for training recurrent neural networks with hidden recurrent states. The leap-frog algorithm does not make use of excessive iterations or tedious line search algorithms to obtain the optimum weight vector of the recurrent network and exhibits the ability to escape for, shallow local minima. Leap-frog is used in off-line mode, when the identification model or predictor is trained, before the neural network is applied in adaptive mode. A comparison is drawn between leap-frog, online gradient descent, and block-mode gradient descent by using a theoretical nonlinear model with known (stable) states.

关键词： Recurrent neural networks backpropagation algorithms Predictive models Management training Africa Robustness State-space methods Nonlinear systems Predictive control System identification

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An intelligent system for integrated predictive diagnosis

An intelligent system for integrated predictive diagnosis

引用

Southeastern Symposium on System Theory

作者： S. Diwakar M.A. Essawy S.Z. Sabatto Department of Electrical and Computer Engineering Tennessee State University Nashville TN USA

We present an automated system for integrated predicted diagnosis. This fault diagnosis method was tested on vibration data recorded from an aft main power transmission of a US Navy CH-46E helicopter. The fault diagnosis system is based on a neuro-fuzzy algorithm. First frequency domain analysis techniques were used to extract features from the vibration signals. These features were then clustered by a self organizing map neural network and identified by a backpropagation network. The decisions from different channels or sensors were fused using fuzzy logic techniques.

关键词： Intelligent systems Fault diagnosis Testing Power transmission Helicopters Clustering algorithms backpropagation algorithms Frequency domain analysis Data mining Feature extraction

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Neural network based adaptive microphone array system for speech enhancement

Neural network based adaptive microphone array system for sp...

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International Joint Conference on Neural Networks (IJCNN)

作者： N. Grbic M. Dahl I. Claesson Department of Signal Processing University of Karlskrona Ronneby Ronneby Sweden

Presents a microphone array system for use in handsfree mobile telephone equipment. The array is based on a fast and efficient "on-site" and "self-calibration" scheme. The performance in suppressing the interior car cabin noise and the far-end speech is approximately 17 dB, respectively, while maintaining the near-end speaker level. The near-end signal is almost undistorted. The performance of two different algorithms, normalized least-mean-square (NLMS) and fully connected backpropagation supervised neural network (MLP-NN) are evaluated. The proposed microphone array calibration scheme can also be used in other situations such as speech recognition devices.

关键词： Microphone arrays Neural networks Adaptive systems Adaptive arrays Telephone equipment Noise level Speech enhancement backpropagation algorithms Calibration Speech recognition

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Exact Hessian calculation in feedforward FIR neural networks

Exact Hessian calculation in feedforward FIR neural networks

引用

International Joint Conference on Neural Networks (IJCNN)

作者： T.J. Cholewo J.M. Zurada Department of Electrical Engineering University of Louisville Louisville KY USA

FIR neural networks are feedforward neural networks with regular scalar synapses replaced by linear finite impulse response filters. This paper introduces the second order temporal backpropagation algorithm which enables the exact calculation of the second order error derivatives for a FIR neural network. This method is based on the error gradient calculation method first proposed by Wan (1993) and referred to as temporal backpropagation. A reduced FIR synapse model obtained by ignoring unnecessary time lags is proposed to reduce the number of network parameters.

关键词： Intelligent networks Finite impulse response filter Neural networks Feedforward neural networks Neurons Multi-layer neural network backpropagation algorithms Ear Nonlinear filters Difference equations

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A projection filter for use with parameterised learning models

A projection filter for use with parameterised learning mode...

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International Conference on Pattern Recognition

作者： M.J.S. Day J.S. Payne Department of Computing Buckinghamshire Chilterns University College High Wycombe Buckinghamshire UK

This paper presents a projection filter for use with parameterised learning models. Two aspects of the projection filter are considered. Firstly, the filter's operation is demonstrated using the perceptron learning rule on a simple two class discrimination problem. Secondly, the projection filter is used to extend the learning capabilities of a nonlinear spatio-temporal neural network model. An experiment was undertaken to compare the effectiveness of applying temporal backpropagation to a multilayer feedforward network employing either finite impulse response (FIR) or projection filters. Results show that the projection filter reached a lower mean square error (MSE) when compared to the FIR version of the network.

关键词： Finite impulse response filter Difference equations Educational institutions Mean square error methods Convergence Recurrent neural networks backpropagation algorithms Chaos Filtering Computer architecture

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A new method to construct FNN with linear output neurons

A new method to construct FNN with linear output neurons

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International Conference on Signal Processing Proceedings (ICSP)

作者： Wang Xiangdong Chen Yongmei Shi Linchu Artificial Neural Network Group Beijing China

In this paper, a new network-growing method for a multilayer feedforward neural network (FNN) is proposed. It has the following distinctive features: 1) The network starts training with a small network and gradually grows its hidden neurons. 2) The activation function of its output neurons is a linear function. Moreover, its application in pattern recognition is also discussed. Simulation results show that the new algorithm achieves a higher recognition rate and converges faster than the conventional backpropagation algorithm and it can avoid the trap of local minima through increasing the hidden neurons.

关键词： Neurons Neural networks backpropagation algorithms Vectors Multi-layer neural network Pattern recognition Feedforward neural networks Network topology Artificial neural networks Gradient methods

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Bipedal trajectory control based on neurofuzzy networks

Bipedal trajectory control based on neurofuzzy networks

引用

IEEE Conference on Control Technology and Applications (CCTA)

作者： Jih-Gau Juang Department of Electrical Engineering University of Missouri Columbia MO USA

ISBN: (纸本)078034104X

This paper presents a bipedal trajectory control technique based on a neurofuzzy controller and a neural network emulator. The neurofuzzy controller is a five-layered neurofuzzy network, it provides the control signals in each stage of a walking gait. The neural network emulator is a conventional three-layered feedforward neural network. It emulates the robotic dynamics and provides the error signals which can be used to back propagate through the controller in each stage. This technique can generate dynamic walking gaits along a pre-specified reference trajectory on sloping terrain.

关键词： Neural networks Legged locomotion Fuzzy systems Feedforward neural networks Robot kinematics Fuzzy control Fuzzy neural networks backpropagation algorithms Nonlinear systems Multi-layer neural network

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