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Maximum-likelihood stochastic matching approach to non-linear equalization for robust speech recognition

Maximum-likelihood stochastic matching approach to non-linea...

引用

International Conference on Spoken Language, ICSLP

作者： A.C. Surendran Chin-Hui Lee M. Rahim Rutgers University USA Bell Laboratories USA AT and T Laboratories USA

We present a new technique in the stochastic matching framework to compensate for nonlinear distortions in speech recognition. The features of the test data and the means of the trained model are both transformed using neural networks to better fit each other. The parameters of the neural network are estimated using a novel combination of the generalized EM (GEM) and the backpropagation algorithms. In the feature transformation case, when the exact Q-functions cannot be calculated, approximations are heuristically derived. The mathematical properties of the new algorithm are analysed. The performance of the algorithm is also studied under different mismatch conditions.

关键词： Stochastic processes Robustness Speech recognition Testing Neural networks Degradation Laboratories Parameter estimation Nonlinear distortion backpropagation algorithms

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Orthonormal function neural network for nonlinear system modeling

Orthonormal function neural network for nonlinear system mod...

引用

International Conference on Neural Networks

作者： I. Scott B. Mlllgrew Department of Electrical Engineering University of Edinburgh Edinburgh UK

Nonlinear system identification is often solved by determining a set of coefficients for a finite number of fixed nonlinear basis functions. However, if the input data is drawn from a high-dimensional space, the number of required basis functions grows exponentially with dimension, and this has led many authors to consider subset model selection techniques. In this paper we describe a one hidden layer neural network which employs a set of signal independent orthonormal expansions, and a scaling derived from an estimate of the vector probability density function of the input data. The orthonormality of the basis functions allows the contribution of each basis function to the model to be calculated independently, since their contribution is directly related to the magnitude of the corresponding weights in the output layer. The resulting neural network retains the desirable linearity in the parameters nature of radial basis function neural networks.

关键词： Neural networks Nonlinear systems Artificial neural networks Integrated circuit modeling Radial basis function networks Probability density function Nonlinear dynamical systems Delay effects Adaptive signal processing backpropagation algorithms

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Nearest neighbor rules PAC-approximate feedforward networks

Nearest neighbor rules PAC-approximate feedforward networks

引用

International Conference on Neural Networks

作者： N.S.V. Rao Oak Ridge National Laboratory Oak Ridge TN USA

The problem of function estimation using feedforward neural networks based on an independently and identically generated sample is addressed. The feedforward networks with a single hidden layer of 1/(1+e/sup -/spl gamma/z/)-units and bounded parameters are considered. It is shown that given a sufficiently large sample, a nearest neighbor rule approximates the best neural network such that the expected error is arbitrarily bounded with an arbitrary high probability. The result is extendible to other neural networks where the hidden units satisfy a suitable Lipschitz condition. A result of practical interest is that the problem of computing a neural network that approximates (in the above sense) the best possible one is computationally difficult, whereas a nearest neighbor rule is linear-time computable in terms of the sample size.

关键词： Nearest neighbor searches Neural networks Computer networks Convergence Feedforward neural networks Artificial neural networks Laboratories Training data backpropagation algorithms Search methods

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Spatio-temporal filtering of the EEG via neural network based multireference adaptive noise cancelling

Spatio-temporal filtering of the EEG via neural network base...

引用

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

作者： C.J. James M.T. Hagan R.D. Jones P.J. Bones G.J. Carroll Department of Electrical and Electronic Engineering University of Canterbury New Zealand School of EIectrical and Computer Engineering Oklahoma State University USA Department of Medical Physics & Bioengineering Christchurch Hospital New Zealand Department of Neurology Christchurch Hospital Christchurch New Zealand

A system is proposed which enhances transient non-stationarities and, in particular, epileptiform discharges in the EEG. It is based around the technique of multireference adaptive noise cancelling (MRANC) which attenuates the background EEG on a primary channel by using spatial and temporal information from adjacent multichannel EEG recording. This implemented by means of a 3-layer perceptron artificial neural network trained by a backpropagation algorithm. System performance was measured as the percentage increase in signal-to-noise ratio (SNR) of predetermined epileptiform discharges in recorded EEG segments. The results obtained show that, due to the nonlinear nature of the artificial neural network, the improvement in SNR is significant when compared to the performance of MRANC utilising a linear model. MRANC is proposed as the first stage of a neural network based multi-stage system to detect epileptiform discharges in the interictal EEG for the diagnosis of epilepsy.

关键词： Filtering Electroencephalography Neural networks Epilepsy Artificial neural networks Signal to noise ratio Noise cancellation backpropagation algorithms System performance Brain modeling

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Hardware-friendly learning algorithms for neural networks: an overview

Hardware-friendly learning algorithms for neural networks: a...

引用

International Conference on Microelectronics for Neural Networks

作者： P.D. Moerland E. Fiesler IDIAP Martigny Switzerland

The hardware implementation of artificial neural networks and their learning algorithms is a fascinating area of research with far-reaching applications. However, the mapping from an ideal mathematical model to compact and reliable hardware is far from evident. This paper presents an overview of various methods that simplify the hardware implementation of neural network models. Adaptations that are proper to specific learning rules or network architectures are discussed. These range from the use of perturbation in multilayer feedforward networks and local learning algorithms to quantization effects in self-organizing feature maps. Moreover, in more general terms, the problems of inaccuracy, limited precision, and robustness are treated.

关键词： Neural networks Neural network hardware Signal processing algorithms Artificial neural networks Circuits backpropagation algorithms Nonhomogeneous media Electronic mail Mathematical model Multi-layer neural network

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Analog integrated circuit parameter fault diagnosis using artificial neural network

Analog integrated circuit parameter fault diagnosis using ar...

引用

International Conference on ASIC

作者： Jingfan Zhang Junren Gan Linsheng Yao Inst. of Metall. Acad. Sinica Shanghai China

An artificial neural network method used for analog IC parameter fault diagnosis is presented in this paper. It is fast and accurate. Therefore it has boundless prospects in the field of analog IC parameter fault diagnosis. With the rapid development in IC technology, the fault diagnosis problem of analog IC has become more acute. The traditional methods' computation complexity and inaccuracy of results make most of them still unacceptable. We therefore research and develop an artificial neural network system to resolving the low velocity and low measurability problem of the traditional methods.

关键词： Analog integrated circuits Fault diagnosis Artificial neural networks Neural networks Computer networks Reflection Circuit simulation Circuit testing Linear circuits backpropagation algorithms

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A new method of radar target recognition based on neural network

A new method of radar target recognition based on neural net...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Zhu Yong Souyong Wang Jiangmin Qin Radar Academy of Air force China

ISBN: (纸本)0780329120

This paper provides, for the first time, a new method for recognizing the number of aircraft in radar targets based on a neural network. The method converts many duplicate cycle intermediate frequency (IF) narrow bandwidth signals received by the radar which scans the targets into a complex envelope signal, calculates its autocorrelation matrix and autocorrelation matrix characteristic values, and uses these as the primary characteristic of radar target recognition. This is done in order to decrease the coherency between the characteristic components, highlight the divergence, reduce the characteristic space dimensions, compress its primary characteristic data through the APEX (adaptive principal component extraction) algorithm, and uses the BP (backpropagation) algorithm to recognize the compressed data as the quadratic characteristic. Recognition trial shows the aircraft number information extracted from the IF narrow bandwidth signal using this method, is influenced little by the targets space position or posture and it has stronger curb noise ability. Applying the APEX algorithm for compression of the primary characteristic data makes the targets characteristic stable and efficient, and results in a relatively good target recognition.

关键词： Target recognition Neural networks backpropagation algorithms Aircraft Bandwidth Autocorrelation Matrix converters Data mining Airborne radar Frequency conversion

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Sensitivity and learning of two digital artificial neural network structures

Sensitivity and learning of two digital artificial neural ne...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： V.E. DeBrunner S.C. Li S. Lewandowsky School of Electrical & Computer Engineering University of Oklahama USA Psychology Max-Planck-Institut Germany Psychology University of Western Australia Australia

We extend the analysis of parameter sensitivity and interdependence to two digital artificial neural network structures, the backpropagation and ALCOVE. This paper compares the two networks, and we generalize to show that a highly sensitive weight contributes more to the prediction of the network than does an insensitive parameter. This suggests that the information structure of an input pattern can be determined by looking at the sensitivity of the interconnection weights, which has ramifications in network design. Additionally, results from a different set of simulations indicate that information about weight sensitivity and interdependence is predictive of the learning behavior of the networks.

关键词： Artificial neural networks Psychology Convergence backpropagation algorithms Testing Predictive models Search methods Parameter estimation Australia Performance evaluation

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A local approach for a fuzzy error function used in multilayer perceptron training through genetic algorithm

A local approach for a fuzzy error function used in multilay...

引用

International Conference on Neural Networks

作者： D. Gueriot E. Maillard Uha Trop Laboratory Mulhouse France ECN GESMA Brest France

We previously (1995) proposed a new error function tailored to train one hidden-layer perceptron for classification problems, using a two-stage mechanism (splitting up the input space into regions which are then combined according to the most representative class of the encompassed patterns). This crisp error function relies on the number of patterns of different classes in a same region. In this paper, we enhance this function by incorporating a fuzzy component which allows all the region boundaries to tend to an optimal Bayesian separation. Genetic algorithms were previously involved in computing the weights of the network. A genetic enhancement is here introduced with a phenotypic based operator, through fresh blood strategies. This method takes advantage of coarse a priori knowledge about the problem to drive more efficiently the population evolution. Numerous tests using our new approach show a dramatic additional improvement compared to the already efficient former one. Interesting perspectives emerge from these performance results associated with the relevant knowledge offered by the region boundaries.

关键词： Multilayer perceptrons Genetic algorithms Electronic mail backpropagation algorithms Convergence Laboratories Bayesian methods Computer networks Blood Testing

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Narrowband direction finding using complex EKF trained multilayered neural networks

Narrowband direction finding using complex EKF trained multi...

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International Conference on Signal Processing Proceedings (ICSP)

作者： K. Deergha Rao Research and Training Unit for Navigational Electronics Osmania University Hyderabad India

A technique using a multilayered neural network has been developed for the narrowband direction finding problem that involves array processing of non-Gaussian signals. The complex extended Kalman filtering algorithm is derived for training the networks with complex input signals. Two networks were implemented, one with third order cumulants and the other with traditional correlations of the received signal vector evaluated at different combinations of directions of arrival (DOAs) as training inputs. Simulation results show that the network trained with cumulants outperforms the network trained with the correlations.

关键词： Narrowband Neural networks Multi-layer neural network Sensor arrays Kalman filters backpropagation algorithms Computer networks Navigation Array signal processing Signal processing

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