By Topic

Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop

Date Aug. 31 1992-Sept. 2 1992

Filter Results

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)
    Freely Available from IEEE
  • Artificial neural network for ECG arryhthmia monitoring

    Publication Year: 1992, Page(s):350 - 359
    Cited by:  Papers (2)
    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»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Neural networks for segmentation and clustering of biomagnetical signals

    Publication Year: 1992, Page(s):343 - 349
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (312 KB)

    When measuring biomagnetic signals the amount of data required is very large due to modern multichannel sensor arrays. Using the example of the magnetocardiogram (MCG), the authors show how these data can be automatically segmented and clustered with the help of neural algorithms. Self-organizing maps are not suitable for this application due to the character of the measured data. The data are com... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 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»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 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»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Adaptive segmentation of textured images using linear prediction and neural networks

    Publication Year: 1992, Page(s):401 - 410
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (624 KB)

    An adaptive technique for classifying and segmenting textured images is presented. This technique uses an efficient least squares algorithm for recursive estimation of two-dimensional autoregressive texture models and neural networks for recursive classification of the models. A network with fixed, but space-varying, interconnection weights is used to optimally select a small representative set of... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Ensemble methods for handwritten digit recognition

    Publication Year: 1992, Page(s):333 - 342
    Cited by:  Papers (18)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (404 KB)

    Neural network ensembles are applied to handwritten digit recognition. The individual networks of the ensemble are combinations of sparse look-up tables (LUTs) with random receptive fields. It is shown that the consensus of a group of networks outperforms the best individual of the ensemble. It is further shown that it is possible to estimate the ensemble performance as well as the learning curve ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Compression of subband-filtered images via neural networks

    Publication Year: 1992, Page(s):382 - 390
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (428 KB)

    A novel architecture for image compression is proposed, which is based on a suitable combination of subband filtering and linear neural networks. This combination permits efficient coding, together with the advantages of the neural-network-based approach. The architecture is described, and results of simulations are presented. The architecture is shown to perform well, notwithstanding the reduced ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Discrete neural networks and fingerprint identification

    Publication Year: 1992, Page(s):316 - 322
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (216 KB)

    The author has developed a general method for discretization of feedforward neural networks and has empirically demonstrated the usefulness of the method by successfully applying it to the nontrivial task of fingerprint identification. Surprisingly, the discrete neural network (DNN) developed in this way demanded just 4 b for the table representation of the sigmoid function, and only 6 b for the r... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Adaptive training of feedback neural networks for non-linear filtering

    Publication Year: 1992, Page(s):550 - 559
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (348 KB)

    The authors propose a general framework which encompasses the training of neural networks and the adaptation of filters. It is shown that neural networks can be considered as general nonlinear filters which can be trained adaptively, i.e., which can undergo continual training. A unified view of gradient-based training algorithms for feedback networks is proposed, which gives rise to new algorithms... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Prediction of chaotic time series using recurrent neural networks

    Publication Year: 1992, Page(s):436 - 443
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (356 KB)

    The authors propose to train and use a recurrent artificial neural network (ANN) to predict a chaotic time series. Instead of training the network with the next sample in the time series as is normally done, a sequence of samples that follows the present sample will be utilized. Dynamical parameters extracted from the time series provide the information to set the length of these training sequence... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Neural network detection of small moving radar targets in an ocean environment

    Publication Year: 1992, Page(s):306 - 315
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (380 KB)

    Small icebergs and pieces of icebergs are virtually undetectable with conventional marine radar systems. The authors describe a detection scheme for such icebergs. The scheme uses the chirplet transform, a wavelet-inspired transform, to generate images of the Doppler-shifted radar returns from icebergs and ocean surfaces. The images are classified using a neural network trained with the backpropag... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 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»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A system identification perspective on neural nets

    Publication Year: 1992, Page(s):423 - 435
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (508 KB)

    The authors review some of the basic system identification machinery to reveal connections with neural networks. In particular, they point to the role of regularization in dealing with model structures with many parameters, and show the links to overtraining in neural nets. Some provisional explanations for the success of neural nets are also offered View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Unsupervised sequence classification

    Publication Year: 1992, Page(s):184 - 193
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (404 KB)

    The authors first introduce a novel approach for unsupervised sequence classification, the competitive sequence learning (CSL) system. The CSL system consists of several extended Kohonen feature maps which are ordered in a hierarchy. The CSL maps develop a representation for subsequences during the training procedure, with an increasing abstraction on the higher maps. The authors apply their appro... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An electronic parallel neural CAM for decoding

    Publication Year: 1992, Page(s):581 - 587
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (296 KB)

    The authors report measurements taken on an electronic neural system configured for content addressable memory (CAM) using a high-capacity architecture. It is shown that Boltzmann and mean-field learning networks can be implemented in a parallel, analog VLSI system. This system was used to perform experiments with mean-field CAM. The hardware settles on a stored codeword in about 10 μs roughly ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Learning of sinusoidal frequencies by nonlinear constrained Hebbian algorithms

    Publication Year: 1992, Page(s):39 - 48
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (432 KB)

    The authors study certain unsupervised nonlinear Hebbian learning algorithms in the context of sinusoidal frequency estimation. If the nonlinearity is chosen suitably, these algorithm often perform better than linear Hebbian PCA subspace estimation algorithms in colored and impulsive noise. One of the algorithms seems to be able to separate the sinusoids from a noisy mixture input signal. The auth... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 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»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Hierarchical perceptron (HiPer) networks for signal/image classifications

    Publication Year: 1992, Page(s):267 - 278
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (496 KB)

    A new class of decision-based neural networks (DBNNs) is introduced. These networks combine the perceptron-like learning rule with a hierarchical nonlinear network structure and are called HiPer nets. Two HiPer net structures are proposed: hidden-node and subcluster structures. The authors explore several variants of HiPer nets based on the different hierarchical structures and basis functions and... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Globally trained neural network architecture for image compression

    Publication Year: 1992, Page(s):289 - 295
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (328 KB)

    The authors discuss the development of a coding system for image transmission based on block-transform coding and vector quantization. Moreover, a classification of the image blocks is performed in the spatial domain. An architecture incorporating both multilayered perceptron and self-organizing feature map neural networks and a block classification is considered to realize the image coding scheme... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Generalization in cascade-correlation networks

    Publication Year: 1992, Page(s):59 - 68
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (348 KB)

    Two network construction algorithms are analyzed and compared theoretically as well as empirically. The first algorithm is the cascade correlation learning architecture proposed by S. E. Fahlman (1990), while the other algorithm is a small but striking modification of the former. Fahlman's algorithm builds multilayer feedforward networks with as many layers as the number of added hidden units, whi... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 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»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 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»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A two-layer Kohonen neural network using a cochlear model as a front-end processor for a speech recognition system

    Publication Year: 1992, Page(s):139 - 148
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (364 KB)

    The authors describe a two-layer neural network speech recognition system based on Kohonen's algorithm. A cochlear model is used as a front-end processor for the system. The basilar membrane is represented by a cascade of 128 digital filters, of which 90 filters fall within the speech bandwidth of 250 Hz to 4 kHz. The outputs of these 90 filters are presented as the input vector to the first layer... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Neural networks and nonparametric regression

    Publication Year: 1992, Page(s):511 - 521
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (676 KB)

    The problem of estimating an unknown function from a finite number of noisy data points is a problem of fundamental importance for many applications in signal processing, machine vision, pattern recognition, and process control. Recently, several new computational techniques for nonparametric regression have been proposed by statisticians and by researchers in artificial neural networks. The autho... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.