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Supervised fuzzy inference network for invariant pattern recognition

Supervised fuzzy inference network for invariant pattern rec...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： H.K. Kwan L.Y. Cai Electrical and Computer Engineering Faculty of Engineering University of Windsor Windsor ONT Canada Numerical Technologies Inc. San Jose CA USA

A supervised fuzzy inference network (FIN) model and its learning algorithm for invariant pattern recognition are presented in this paper. This fuzzy inference network is suitable for 2-D visual pattern recognition problems and has been tested with letter patterns of black and white pixel values. In contrast to most of the conventional pattern recognition systems, the proposed fuzzy inference network for pattern recognition does not require any pre-processing of feature extraction. Instead, the feature extraction step is incorporated in the structure of the network. The learning speed of the proposed fuzzy inference network is fast. The structure of the proposed fuzzy inference network is simple and it performs well when applied in invariant pattern recognition problems.

关键词： Fuzzy neural networks Pattern recognition Neural networks Character recognition Fuzzy sets Fuzzy systems Feature extraction Neurons backpropagation algorithms Fuzzy logic

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Time series for currency exchange rate of the Brazilian Real

Time series for currency exchange rate of the Brazilian Real

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Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering (CIFEr)

作者： M.A. Bittencourt F.C. Lin Institução de Computação Universidade Federal Fluminense Niteroi Rio de Janeiro Brazil

In our global village the currency exchange rate of a country is considered by international investors as an important yardstick for measuring the health of its economy. In the paper an analysis is made of the Brazilian Real using three different methodologies: the Box-Jenkins or SARIMA; exponential smoothing; and a backpropagation neural network trained by the Levenberg-Marquardt algorithm. Not surprisingly, our study indicates that given the same input data different paradigms yield different results. However, presumably due to the intervention of the Central Bank, the time series exhibits quasiperiodic behaviour. Extrapolations are made into the future. Possible implications are discussed. Our methodology can be applied to any currency extant.

关键词： Exchange rates Smoothing methods Econometrics Neural networks Algorithm design and analysis backpropagation algorithms Extrapolation Computer science Centralized control Data analysis

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Using neural networks with wavelet transforms for an automated mammographic mass classifier

Using neural networks with wavelet transforms for an automat...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： L.M. Bruce N. Shanmugam Department of Electrical and Computer Engineering University of Nevada Las Vegas Las Vegas NV USA

The objective of this paper is to demonstrate the utility of artificial neural networks, in combination with wavelet transforms for the classification of mammogram mass shapes as round or irregular. The discrete wavelet transform is applied to the radial distance measure of the mass shapes, and scalar-energy features are computed to form the input to the neural network classifier. A two-layer neural network with a backpropagation algorithm was trained to differentiate between two classes of mass shapes that were determined by an expert radiologist. The neural network was trained on the wavelet-based feature vectors extracted from the mammogram mass shapes for both manually and automated-segmented data. The performance of the classification system was studied using receiver operating characteristic analysis. It has been shown that the Haar mother wavelet extracted a set of features from the automated-segmented data to obtain sensitivity and specificity of 100%.

关键词： Neural networks Wavelet transforms Artificial neural networks Discrete wavelet transforms Shape measurement Data mining Computer networks backpropagation algorithms Feature extraction Performance analysis

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Fast computation of scalp potentials in response to current sources within the brain using an artificial neural network

Fast computation of scalp potentials in response to current ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： M. Sun R.J. Sclabassi Laboratory of Computational Neuroscience Department of Neurosurgery and Department of Electrical Engineering University of Pittsburgh Pittsburgh PA USA

ISBN: (纸本)0780364651

High-resolution EEG, in which data are recorded from tens or hundreds of electrodes simultaneously, has been widely applied to basic and clinical neuroscience. The data acquired can be utilized to computationally localize functional activity within the brain. In this approach the forward computation, which produces scalp potentials in response to assumed dipolar current sources within the brain, is a key problem. This paper investigates a novel computational algorithm utilizing a backpropagation artificial neural network (ANN). The ANN is pre-trained with thousands of numerically computed potential vector pairs. In each pair, one vector is computed by a fixed spherical head model and the other by a variable nonspherical head model. Once the training is complete, the ANN is able to map forward solutions between these two models, and the traditionally expensive forward computing based on a nonspherical head model of variable shape can be implemented at the low cost of the fixed spherical head model. Our experimental results show that this algorithm is highly accurate, and the improvement on computational speed is so great that real-time modeling of the high-resolution EEG can be realized.

关键词： Scalp Artificial neural networks Brain modeling Electroencephalography Electrodes Neuroscience Computer networks backpropagation algorithms Shape Costs

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On the composite squared-error algorithm for neural networks

On the composite squared-error algorithm for neural networks

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： S.L. Netto M.L.R. de Campos Programa de Engenharia El ktrica Coppe Del Ee Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil

The composite squared-error (CSE) algorithm results from the combination of the backpropagation algorithm with the pseudo-linear algorithm of Scalero and Tepedelenlioglu (1992). It is observed that the CSE algorithm is able to avoid suboptimal solutions and associated saddle points, thus achieving lower values of the error function than the pseudo-linear algorithm, in fewer iterations than the backpropagation algorithm. In this paper, we investigate the implementation of the CSE algorithm with the concepts of momentum gain, time-varying learning rate, and time-varying combining factor. It is verified that these features can improve the overall properties of the CSE convergence process.

关键词： Neural networks Signal processing algorithms backpropagation algorithms Neurons Error correction Signal processing Feedforward neural networks Steady-state Convergence

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A modified actor-critic reinforcement learning algorithm

A modified actor-critic reinforcement learning algorithm

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： S.M. Mustapha G. Lachiver Department of Electrical Engineering and Computer Engineering Université de Sherbrook Sherbrooke QUE Canada

This paper proposes a fast and efficient actor-critic reinforcement learning algorithm that is novel in at least two ways: it updates the critic only when the best action is executed and it takes full advantage of the powerful temporal difference (TD) prediction method to train a continuous-valued actor. Both actor and critic are represented separately by two adaptive neural fuzzy systems tuned by a backpropagation algorithm. While the critic adapts to the actor by minimizing the quadratic sum of TD error, the actor adapts to the critic, by not only using the TD error, but also by using the state value function. The new actor-critic architecture is applied to an inverted pendulum system, which is widely used to compare reinforcement learning architectures.

关键词： State estimation Power engineering computing Power engineering and energy Prediction methods Adaptive systems Fuzzy systems backpropagation algorithms Delay Learning systems Stochastic processes

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Advances in GPR data processing for antipersonnel landmine detection

Advances in GPR data processing for antipersonnel landmine d...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： N.V. Budko R.F. Remis P.M. van Den Berg Laboratory of Electromagnetic Research Centre for Technical Geoscience Delft University of Technnology Delft Netherlands

The problem of antipersonnel landmine detection using the Ground Penetrating Radar (GPR) is discussed. Advanced data-processing algorithms are proposed attempting to solve the arisen inverse scattering problem. In particular, we consider the Method of Image Moments and the Effective Inversion approach. Tests with synthetic and experimental data are presented.

关键词： Ground penetrating radar Data processing Landmine detection Inverse problems Testing Electromagnetic scattering backpropagation algorithms Synthetic aperture radar Radar scattering Image quality

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Development and analysis of a fuzzy identifier for nonlinear discrete systems

Development and analysis of a fuzzy identifier for nonlinear...

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American Control Conference (ACC)

作者： H.M. Emara A.L. Elshafei A. Bahgat Faculty of Engineering Cairo University Giza Egypt

The paper presents an algorithm for fuzzy logic identification of nonlinear systems. The proposed algorithm uses a linear in parameters identifier to avoid the problem of local minima. The stability of the proposed algorithm is proved and a sufficient condition on system excitation to ensure identifiability is presented. The algorithm is validated by comparing its performance with neural networks and backpropagation trained fuzzy identifiers.

关键词： Fuzzy systems backpropagation algorithms Fuzzy logic Nonlinear systems Stability Sufficient conditions Neural networks Inference algorithms Convergence Fuzzy neural networks

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A comparison among weight initialization methods for multilayer feedforward networks

A comparison among weight initialization methods for multila...

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International Joint Conference on Neural Networks (IJCNN)

作者： M. Fernandez-Redondo C. Hernandez-Espinosa Edificio TI Departamento de Informática Universidad Jaume I Castellon Spain Universitat Jaume I Castello de la Plana Comunitat Valenciana ES

In this paper we present the results of a comparison among six different weight initialization methods with two training algorithms and six databases. The comparison is performed by measuring the three following aspects: speed of convergence, generalization and probability of convergence. The two training algorithms are backpropagation (BP) and another one that uses conjugate gradient and dynamical learning rate adaptation (NE). We found the best weight initialization scheme for the (BP) algorithm. The speed of convergence can be improved with respect to the usual initialization, but the two other aspects are similar. For the NE algorithm it is concluded that its performance depends on the initialization much more than BP. Its generalization and probability of convergence can be considered lower than BP and the different weight initialization schemes could not improve this drawback. On the other hand it is faster.

关键词： Nonhomogeneous media Convergence backpropagation algorithms Databases Performance evaluation Velocity measurement Neural networks Bibliographies Concrete Probability distribution

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A fast hybrid algorithm of global optimization for feedforward neural networks

A fast hybrid algorithm of global optimization for feedforwa...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Jiang Minghu Zhu Xiaoyan Yuan Baozong Tang Xiaofang Lin Biqin Ruan Qiuqi Jiang Mingyan Institute of Information Sceince Northern Jiaotong University Beijing China Department of Electronic ShanDong University Jinan China

This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks (MLFNN). The effect of inexact line search on conjugacy was studied and a generalized conjugate gradient method based on this effect was proposed and shown to have global convergence for error backpropagation of MLFNN. The descent property and global convergence was given for the improved hybrid algorithm of the conjugate gradient algorithm, the results of the proposed algorithm show a considerable improvement over the Fletcher-Reeves algorithm and the conventional backpropagation (BP) algorithm, it overcomes the drawback of conventional BP and Polak-Ribieve conjugate gradient algorithm that maybe plunge into local minima.

关键词： Neural networks Feedforward neural networks backpropagation algorithms Multi-layer neural network Convergence Acceleration Hybrid intelligent systems Intelligent networks Computer networks Gradient methods

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