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检索条件"主题词=Backpropagation Algorithms"

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algorithms for feature selection: An evaluation

Algorithms for feature selection: An evaluation

引用

International Conference on Pattern Recognition

作者： D. Zongker A. Jain Department of Computer Science Michigan State University East Lansing MI USA

A large number of algorithms have been proposed for doing feature subset selection. The goal of this paper is to evaluate the quality of feature subsets generated by the various algorithms, and also compare their computational requirements. Our results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. This paper also illustrates the dangers of using feature selection in small sample size situations. It gives the results of applying feature selection to land use classification of SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. Application of feature selection to classification of handprinted characters illustrates the value of feature selection in reducing the number of features needed for classifier design.

关键词： Taxonomy Stochastic processes backpropagation algorithms Computer science Testing Pattern recognition Genetic algorithms Nonhomogeneous media

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Modelling of biological neurons by artificial neural networks

Modelling of biological neurons by artificial neural network...

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IEEE Convention of the Electrical and ELectronic Engineers in Israel

作者： P. Tandeitnik H. Guterman Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beersheba Israel

A general approach for modeling biological neurons by means of ANNs is presented. This approach does not require any a-priori information about the system dynamics. A close-loop neural network architecture based on a multilayer feed forward network, is used as an alternative approach to standard system identification. The model can be employed to simulate the behavior of complex networks.

关键词： Biological system modeling Neurons Artificial neural networks backpropagation algorithms Neural networks Biological neural networks Multi-layer neural network Feedforward neural networks Feeds Brain modeling

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Representation of fuzzy finite state automata in continuous recurrent, neural networks

Representation of fuzzy finite state automata in continuous ...

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International Conference on Neural Networks

作者： C.W. Omlin K.K. Thornber C.L. Giles NEC Research Institute Inc. Princeton NJ USA University of Maryland UMIACS College Park MD USA

Based on previous work on encoding deterministic finite-state automata (DFA) in discrete-time, second-order recurrent neural networks with sigmoidal discriminant functions, we propose an algorithm that constructs an augmented recurrent neural network that encodes fuzzy finite-state automata (FFA). Given an arbitrary FFA, we apply an algorithm which transforms the FFA into an equivalent deterministic acceptor which computes the fuzzy string membership function. The neural network can be constructed such that it recognizes strings of fuzzy regular languages with arbitrary accuracy.

关键词： Fuzzy neural networks Automata Intelligent networks Recurrent neural networks Fuzzy systems Neural networks Artificial neural networks Neurons backpropagation algorithms Fuzzy logic

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Hybrid HMM-MLP classifier for prosthetic arm control purpose

Hybrid HMM-MLP classifier for prosthetic arm control purpose

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IEEE Region 10 International Conference TENCON

作者： Jangrvoo Kwon Chulkyu Shin Eunghyuk Lee Younghwan Han Seunghong Hong Department of Electronic Engineering Inha University Incheon South Korea Department of Computer Engineering Konyang University Nonsan Choongchungnam South Korea Department of Computer Science Sangji University Wonju South Korea

ISBN: (纸本)0780336798

This paper describes an approach for classifying electromyographic (EMG) signals using multilayer perceptrons (MLPs) and hidden Markov models (HMMs) hybrid classifier. Instead of using MLPs as probability generators for HMMs we propose to use MLPs as the second classifiers to increase the discrimination rates of myoelectric patterns. This strategy is proposed to overcome weak discrimination and to consider the dynamic properties of EMG signals. Three discrimination strategies (DHMM, cascaded DHMMs, and HMM with three subnet MLPs) for discriminating signals representative of 6 primitive class of motions are described and compared. The proposed strategy increase the discrimination results considerably. Results are presented to support this approach.

关键词： Hidden Markov models Prosthetics Electromyography backpropagation algorithms Cost function Topology Equations Electrodes Band pass filters Pattern matching

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Experiments on estimating random mapping

Experiments on estimating random mapping

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International Conference on Neural Networks

作者： K.M. Ho C.J. Wang Department of Computer Science University of Essex Colchester UK

The generic function of a feedforward multilayer perceptron (MLP) network is to map patterns from one space to another. This mapping function, determined by the set of examples used to train the network, may be viewed as a hash function. This paper reports the experiments on using a backpropagation MLP network with a dynamic hidden layer to estimate a random mapping from the input to output space and use this estimated mapping as a hash function for a given population of keys. Comparative studies show that the MLP estimated hash functions performs robustly over various population of scarce hash keys which would cause uneven distributions with some traditional hash function.

关键词： Testing Binary codes backpropagation algorithms Computer science Multilayer perceptrons Robustness Neural networks Feedforward neural networks Acceleration

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Neural networks for learning human facial features from labeled graph models

Neural networks for learning human facial features from labe...

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Australian and New Zealand Conference on Intelligent Information Systems

作者： A.R. Mirhosseini H. Yan Department of Electrical Engineering University of Sydney NSW Australia

A novel method to extract human facial features is proposed based on a two level model-object matching paradigm. A facial feature is modeled by a hybrid technique using labeled graph templates that allows incorporating the relational information of the objects not the models. Gabor filters are used to extract low-level image features to label the vertices of a graph template. A backpropagation artificial neural network is used to extract the optimum template model from examples and to classify image blocks as object or non-object classes. The performance of the algorithm is tested in locating the left and right eyes in a set of human face images with large perspective variations. The attractive feature of the algorithm is the reasonable size of the feature vector in comparison to gray level based methods and the inherent parallelism in both image feature extraction and object detection algorithms.

关键词： Neural networks Humans Facial features Data mining Feature extraction Gabor filters Artificial neural networks backpropagation algorithms Testing Eyes

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Finding time series among the chaos: stochastics, deseasonalization, and texture-detection using neural nets

Finding time series among the chaos: stochastics, deseasonal...

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

作者： P. Werbos Nat Sci. Found. USA

Summary form only given, substantially as follows. Problems of portfolio management have included several fundamental time-series problems. Parts of these problems are involved with the inevitable noisiness of financial data, parts with interactions and mode-locking among measures, and parts with the basic probabilistic nature of predictive systems in a rich environment. Modern neural networks have been used, to limited effect, to resolve them. Innovative techniques should prove more helpful. Among the fundamental issues for comprehending time series data are: (1) adjusting models dynamically, as errors emerge and corrections are identified; (2) promoting model-wide adjustment; (3) avoiding the tendency of least-squares forecasts to decay with time; (4) locating the range of plausible outcomes; and (5) complex prediction/correction optimization strategies. Techniques pioneered in neural networks have addressed each of these issues. The most common algorithms employed have been backpropagation variants. Recent advances in backpropagation make possible substantial improvements in identifying seasonality, modality and structural stability. Advances in recurrent networks allow context-sensitive adjustment of sharing and "elastic fuzziness", and new forms of reinforcement learning which permit the detection of interaction among dimensions and dynamic adjustment to that interaction. Reconstruction of priors and "deconstruction" of observer effects are also consequences of elastic fuzzy networks and dual heuristic programming.

关键词： Chaos Stochastic processes Neural networks Predictive models Portfolios Working environment noise Error correction backpropagation algorithms Structural engineering Learning

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Two point training for complex plane transformation

Two point training for complex plane transformation

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International Conference on Neural Networks

作者： Jingyan Song Yeung Yam Department of Mechanical and Automation Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

The complex neural network whose weights, threshold values, inputs and outputs signals are all complex variables is studied in this paper. It is shown that using a training set of only two points in the complex plane,... 详细信息

ISBN: (纸本)0780332105

关键词： Neural networks Signal processing algorithms Neurons Multi-layer neural network backpropagation algorithms Automation Signal processing Least squares approximation Algorithm design and analysis Zirconium

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On incorporating finite impulse response neural network with finite difference time domain method for simulating electromagnetic problems

On incorporating finite impulse response neural network with...

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Antennas and Propagation Society International Symposium

作者： Chen Wu Minh Nguyen J. Litva Wireless Technique Group Communications Research Laboratory McMaster University Hamilton ONT Canada

The finite difference time domain method (FDTD) is a very powerful numerical method to solve electromagnetic (EM) problems. It is very flexible to simulate the problems which have very complex boundaries. It is well known that FDTD method requires long computation time for solving the resonant or high-Q passive structures. The reason for this is because the algorithm is based on the leap-frog formula. For EM modeling is very important to speed up the simulation. The finite impulse response neural network (FIR NN) is applied as a nonlinear predictor to predict time series signal for speeding up the FDTD simulations. The FIR NN is trained by temporal backpropagation learning algorithm. A waveguide filter is used as an example and simulated by the FDTD method. It demonstrates that a short segment of an FDTD data is used to train the predictor, and the predictor can predict later information very well. The total least square (TLS) method is used as a predictor as well. By comparing the predicted error, it is shown that FIR neural network gives better prediction than that of the TLS.

关键词： Neural networks Finite difference methods Time domain analysis Finite impulse response filter Computational modeling backpropagation algorithms Resonance Predictive models Electromagnetic waveguides Least squares methods

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Nonlinear predictive modeling using dynamic non-singleton fuzzy logic systems

Nonlinear predictive modeling using dynamic non-singleton fu...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： G.C. Mouzouris J.M. Mendel Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

We investigate the dynamic versions of fuzzy logic systems (FLSs), and specifically their nonsingleton generalizations (NSFLSs), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. Since dynamic NSFLSs can be considered to belong to the family of general nonlinear autoregressive moving average (NARMA) models, they are capable of parsimoniously modeling NARMA processes. We study the performance of both dynamic and static FLSs in the predictive modeling of a NARMA process.

关键词： Predictive models Fuzzy logic Nonlinear dynamical systems Uncertainty Output feedback Signal processing Image processing Fuzzy systems backpropagation algorithms Neural networks

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