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Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop

Aug. 31 1992-Sept. 2 1992

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Displaying Results 1 - 25 of 64
  • Neural Networks for Signal Processing II. Proceedings of the IEEE-SP Workshop (Cat. No.92TH0430-9)

    Publication Year: 1992
    Request permission for commercial reuse | PDF file iconPDF (32 KB)
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  • A fast simulator for neural networks on DSPs or FPGAs

    Publication Year: 1992, Page(s):597 - 605
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (380 KB)

    The authors present a description of their achievements and current research on the implementation of a fast digital simulator for artificial neural networks. This simulator is mapped either on a parallel digital signal processor (DSP) or on a set of field programmable gate arrays (FPGAs). Powerful tools have been developed that automatically compile a graphical neural network description into exe... View full abstract»

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  • Adaptive template method for speech recognition

    Publication Year: 1992, Page(s):103 - 110
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (228 KB)

    An adaptive template method for pattern recognition is proposed. The template adaptation algorithm is derived based on minimizing the classification error of the classifier. The authors have applied this method to a multispeaker English E-set recognition experiment and achieved a 90.38% average recognition rate with only one template for each letter. This indicates that the derived templates are a... View full abstract»

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  • Real time CCD-based neural network system for pattern recognition applications

    Publication Year: 1992, Page(s):606 - 616
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (588 KB)

    A generic NNC (neural network classifier) capable of providing 1.9 billion programmable connections per second is described. Applications for these generic processors include image and speech recognition as well as sonar signal identification. To demonstrate the modularity and flexibility of the CCD (charge coupled device) NNCs, two generic multilayer system-level boards capable of both feedforwar... View full abstract»

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  • Fuzzy partition models and their effect in continuous speech recognition

    Publication Year: 1992, Page(s):111 - 120
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (312 KB)

    Fuzzy partition models (FPMs) with multiple input-output units were applied to continuous speech recognition, and the use of automatic incremental training was evaluated. After initial training using word data, phrase recognition rates of 72.7% and 66.9% were obtained for an FPM and a TDNN (time-delay neural network), respectively. After incremental training, the phrase recognition rates improved ... View full abstract»

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  • Minimal classification error optimization for a speaker mapping neural network

    Publication Year: 1992, Page(s):233 - 242
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (368 KB)

    The authors prepose a novel optimization technique for speaker mapping neural network training using the minimal classification error criterion. The conventional speaker mapping neural networks were trained under minimal distortion criteria. The minimal classification error optimization technique is applied to train the speaker mapping neural network. The authors describe the speaker mapping neura... View full abstract»

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  • Inserting rules into recurrent neural networks

    Publication Year: 1992, Page(s):13 - 22
    Cited by:  Papers (16)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (444 KB)

    The authors present a method that incorporates a priori knowledge in the training of recurrent neural networks. This a priori knowledge can be interpreted as hints about the problem to be learned and these hints are encoded as rules which are then inserted into the neural network. The authors demonstrate the approach by training recurrent neural networks with inserted rules to learn to recognize r... View full abstract»

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  • Empirical risk optimisation: neural networks and dynamic programming

    Publication Year: 1992, Page(s):121 - 130
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (436 KB)

    The authors propose a novel system for speech recognition which makes a multilayer perceptron and a dynamic programming module cooperate. It is trained through a cost function inspired by learning vector quantization which approximates the empirical average risk of misclassification. All the modules of the system are trained simultaneously through gradient backpropagation; this ensures the optimal... View full abstract»

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  • On the identification of phonemes using acoustic-phonetic features derived by a self-organising neural network

    Publication Year: 1992, Page(s):243 - 252
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (376 KB)

    A self-organizing neural network (SONN) is subjected to a training and calibration process using continuous speech spoken by three talkers. The aim of this process is to establish a system which is able to transform speech frame cepstrum vectors into vectors of continuous valued acoustic-phonetic features. The calibration process also involves a stage where each neuron of the SONN is assigned a ve... View full abstract»

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  • On the complexity of neural networks with sigmoidal units

    Publication Year: 1992, Page(s):23 - 28
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (252 KB)

    Novel techniques based on classical tools such as rational approximation and harmonic analysis are developed to study the computational properties of neural networks. Using such techniques, one can characterize the class of function whose complexity is almost the same among various models of neural networks with feedforward structures. As a consequence of this characterization, for example, it is ... View full abstract»

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  • Constructing neural networks for contact tracking

    Publication Year: 1992, Page(s):560 - 569
    Cited by:  Papers (3)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (432 KB)

    A neural network approach for contact state estimation is presented. This neural network, NICE (neurally inspired contact estimation), has been constructed to directly embody the major problem domain constraint of uniform contact velocity and heading. NICE networks are constructed, not trained, to estimate contact position and motion from angle-of-arrival (AOA) measurements. The major advantages o... View full abstract»

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  • Artificial neural network for ECG arryhthmia monitoring

    Publication Year: 1992, Page(s):350 - 359
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (400 KB)

    The application of a multilayer perceptron artificial neural network model (ANN) to detect the QRS complex in ECG (electrocardiography) signal processing is presented. The objective is to improve the heart beat detection rate in the presence of severe background noise. An adaptively tuned multilayer perceptron structure is used to model the nonlinear, time-varying background noise. The noise is re... View full abstract»

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  • Application of frequency-domain neural networks to the active control of harmonic vibrations in nonlinear structural systems

    Publication Year: 1992, Page(s):474 - 483
    Cited by:  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (352 KB)

    The authors show how a nonlinear adaptive controller of quasi-neural architecture can be used to control harmonic vibrations even when it has to act through a nonlinear actuator element. The controller comprises a fixed nonlinearity to generate harmonics of the sinusoidal reference signal and a linear adaptive combiner. The coefficients in the adaptive combiner are adjusted using a steepest descen... View full abstract»

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  • Text-independent talker identification system combining connectionist and conventional models

    Publication Year: 1992, Page(s):131 - 138
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (396 KB)

    Several techniques have been used for speaker identification which have different characteristics and capabilities. The respective merits of three different systems respectively employing neural networks, hidden Markov models, and multivariate autoregressive models are compared. A novel text-independent speaker identification system based on the cooperation of these different techniques is present... View full abstract»

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  • Robust identification of human face using mosaic pattern and BPN

    Publication Year: 1992, Page(s):296 - 305
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (532 KB)

    The backpropagation network (BPN) is applied to human face recognition. A mosaic pattern transformed from the central part of a human face image is put into the BPN for personal identification. This combination succeeds in recognition of hundreds of people with robustness not only for defocused or noisy images but also for images of different face expressions or different ages. Hidden units of the... View full abstract»

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  • Supervised learning on large redundant training sets

    Publication Year: 1992, Page(s):79 - 89
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (404 KB)

    A novel algorithm combining the good properties of offline and online algorithms is introduced. The efficiency of supervised learning algorithms on small-scale problems does not necessarily scale up to large-scale problems. The redundancy of large training sets is reflected as redundancy gradient vectors in the network. Accumulating these gradient vectors implies redundant computations. In order t... View full abstract»

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  • Spectral representations for speech recognition by neural networks-a tutorial

    Publication Year: 1992, Page(s):214 - 222
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (308 KB)

    Spectrum-based speech representations are discussed. Spectral representations, in order to be useful for speech recognition, need to be justified from both the computational (analytical) and the perceptual viewpoints. The authors' discussion of spectral representations, therefore, includes both the computational model and the associated measures of similarity that are appropriate for neural networ... View full abstract»

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  • Dispersive networks for nonlinear adaptive filters

    Publication Year: 1992, Page(s):540 - 549
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (364 KB)

    The authors describe a dispersive network architecture that can be used for nonlinear adaptive channel equalization and signal prediction. Dispersive networks contain internal delay elements that spread out features in the input signal over time and space, so that they influence the output at multiple points in the future. When used for equalization, these networks can compensate for nonlinear cha... View full abstract»

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  • Classification of simulated radar imagery using lateral inhibition neural networks

    Publication Year: 1992, Page(s):279 - 288
    Cited by:  Papers (4)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (412 KB)

    The use of neural networks for the classification of simulated inverse synthetic aperture radar imagery is investigated. Symmetries of the artificial imagery make the use of localized moments a convenient preprocessing tool for the inputs to a neural network. A database of simulated targets was obtained by warping dynamical models to representative angles and generating images with differing targe... View full abstract»

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  • Capacity control in classifiers for pattern recognition

    Publication Year: 1992, Page(s):255 - 266
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (460 KB)

    Achieving good performance in statistical pattern recognition requires matching the capacity of the classifier to the size of the available training set. A classifier with too many adjustable parameters (large capacity) is likely to learn the training set without difficulty, but be unable to generalize properly to new patterns. If the capacity is too small, even the training set might not be learn... View full abstract»

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  • A generalization error estimate for nonlinear systems

    Publication Year: 1992, Page(s):29 - 38
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (376 KB)

    A new estimate (GEN) of the generalization error is presented. The estimator is valid for both incomplete and nonlinear models. An incomplete model is characterized in that it does not model the actual nonlinear relationship perfectly. The GEN estimator has been evaluated by simulating incomplete models of linear and simple neural network systems. Within the linear system GEN is compared to the fi... View full abstract»

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  • An adaptive neural network model for distinguishing line- and edge detection from texture segregation

    Publication Year: 1992, Page(s):391 - 400
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (468 KB)

    The authors consider an important paradigm in vision: distinguishing object contours or edges (and lines) from object surface textures. To accomplish this, an artificial neural network model, called the EDANN model, is used for both texture segregation and line and edge detection starting from a common bank of spatial filters. The model provides different representations of a retinal image in such... View full abstract»

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  • Adaptive decision-feedback equalizer using forward-only counterpropagation networks for Rayleigh fading channels

    Publication Year: 1992, Page(s):570 - 578
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (272 KB)

    A forward-only counterpropagation network (FCPN) is proposed for nonlinear equalization of digital transmission channels. The FCPN is a type of multilayer feedforward network proposed by Hecht-Nielsen. Its learning mechanism is a combination of unsupervised self-organizing and supervised training. A decision-feedback equalizer based on FCPN was simulated on a digital computer. The results show tha... View full abstract»

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  • Nonlinear system identification using multilayer perceptrons with locally recurrent synaptic structure

    Publication Year: 1992, Page(s):444 - 453
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (388 KB)

    It is proved that a multilayer perceptron (MLP) with infinite impulse response (IIR) synapses can represent a class of nonlinear block-oriented systems. This includes the well-known Wiener, Hammerstein, and cascade or sandwich systems. Previous methods used to model these systems such as the Volterra series representation are known to be extremely inefficient, and so the IIR MLP represents an effe... View full abstract»

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  • A neural feedforward network with a polynomial nonlinearity

    Publication Year: 1992, Page(s):49 - 58
    Cited by:  Patents (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (308 KB)

    A novel neural network based on the Wiener model is proposed. The network is composed of a hidden layer of preprocessing neurons followed by a polynomial nonlinearity and a linear output neuron. The author tries to solve the problem of finding an appropriate preprocessing method by using a modified backpropagation algorithm. It is shown by the use of calculation trees that the proposed approach is... View full abstract»

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