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Performance of adaptive Bayesian equalizers in outdoor environments

Performance of adaptive Bayesian equalizers in outdoor envir...

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IEEE Conference on Vehicular Technology (VTC)

作者： J.L. Valenzuela F. Casadevall Departament de Teoria del Senyal i Comunicacions E.T.S.E.B. Universitat Politecnica de Catalunya Barcelona Spain

Outdoor communications are affected by multipath propagation that imposes an upper limit on the system data rate and restricts possible applications. In order to overcome the degrading effect introduced by the channel, conventional equalizers implemented with digital filters have been traditionally used. A new approach based on neural networks is considered. In particular, the behavior of the adaptive Bayesian equalizer implemented by means of radial basis functions applied to the channel equalization of radio outdoor environments has been analyzed. The method used to train the equalizer coefficients is based on a channel response estimation. We compare the results obtained with three channel estimation methods: the least sum of square errors (LSSE) channel estimation algorithm, recursive least square (RLS) algorithm employed only to obtain one channel estimation and, finally, the RLS algorithm used to estimate the channel every decided symbol for the whole frame.

关键词： Bayesian methods backpropagation algorithms Decision feedback equalizers Maximum likelihood estimation Channel estimation Clustering algorithms Delay Power system modeling Degradation Intersymbol interference

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Optimal pruning of neural tree networks for improved generalization

Optimal pruning of neural tree networks for improved general...

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International Joint Conference on Neural Networks (IJCNN)

作者： A. Sankar R.J. Mammone CAIP Center and Department of Electrical Engineering Rutgers University Piscataway NJ USA

An optimal pruning algorithm for neural tree networks (NTN) is presented. The NTN is grown by a constructive learning algorithm that decreases the classification error on the training data recursively. The optimal pruning algorithm is then used to improve generalization. The pruning algorithm is shown to be computationally inexpensive. Simulation results on a speaker-independent vowel recognition task are presented to show the improved generalization using the pruning algorithm.< >

关键词： Neural networks Neurons Training data Tree data structures Classification tree analysis Speech recognition backpropagation algorithms Binary trees Classification algorithms Feedforward neural networks

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Determining the influence of input parameters on BP neural network output error using sensitivity analysis

Determining the influence of input parameters on BP neural n...

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International Conference on Computational Intelligence and Multimedia Applications

作者： F. Frost V. Karri DCC Offices Comalco Aluminium Limited Bell Bay TAS Australia School of Science and Engineering University of Tasmania Hobart TAS Australia

Neural networks, due to their excellent capabilities for modelling process behaviour, are gaining precedence over traditional empirical modelling techniques, such as statistical methods. While neural networks have good ability to map any reasonable continuous function, they do not easily explain how the inputs are related to an output, and also whether the selected inputs have any significant relationship with an output. There is quite often a need to identify some order of influence of the input variables on the output variable. In this paper, a technique for determining the order of influence of the n elements of the input vector on the m elements of the output vector is presented and discussed. While a sample mathematical function is used to introduce the technique, a more practical application of this method in the aluminium smelting industry is considered. It is shown that, using a sensitivity analysis on the backpropagation (BP) algorithm, the degree of influence of the input parameters on the output error can be successfully estimated.

关键词： Neural networks Sensitivity analysis Input variables Training data Aluminum Australia Sorting Metals industry backpropagation algorithms Manufacturing

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Mandarin tone recognition by multi-layer perceptron

Mandarin tone recognition by multi-layer perceptron

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： P.-C. Chang S.-W. Sun S.-H. Chen Telecommunication Labs. Ministry of Communications Taiwan Department of Communication Engineering National Chiao Tung University Taiwan

Tone recognition of isolated Mandarin monosyllables using the multilayer perceptron (MLP) model is reported. Ten features extracted from the fundamental frequency and energy contours of a monosyllable are used as the recognition features. The backpropagation algorithm is used to train the internal representation of the MLP. Several variations of the MLP with regard to the number of layers and the number of neurons in each layer are considered. In a speaker-untrained test, a recognition rate of 93.8% was achieved. It outperforms a Gaussian classifier which has a recognition rate of 90.6%.< >

关键词： Multilayer perceptrons Feature extraction Frequency backpropagation algorithms Neurons Testing

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Artificial neural net approach for capacitor placement in power system

Artificial neural net approach for capacitor placement in po...

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International Forum on Applications of Neural Networks to Power Systems

作者： P.K. Dash S. Saha P.K. Nanda Energy Research Centre Department of Electrical Engineering R. E. College Rourkela India

The authors propose a new methodology for controlling multitap capacitors in a power system using a three layer feedforward neural network. The neural network, in the proposed scheme is separately trained with two algorithms namely backpropagation and a combined backpropagation-Cauchy's learning algorithm. Studies on 30 bus IEEE test system are carried out and quite satisfactory results are obtained. The inputs to the net are the real power, reactive power and voltage magnitude at a few selected buses and the network's outputs are the values of capacitive Var injection. Performance comparison is made between two algorithms and the combined backpropagation-Cauchy's algorithm is found to be better than the other.< >

关键词： Artificial neural networks Power capacitors backpropagation algorithms Reactive power Control systems Power systems Neural networks Feedforward neural networks System testing Voltage

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KORA-2: a new cache replacement policy and its performance

KORA-2: a new cache replacement policy and its performance

引用

IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： H. Khalid M.S. Obaidat KFUPM and Motorola Monmouth University USA

In this paper, we propose a new strategy (KORA-2) for the replacement of lines in cache memories. The algorithm is efficient and easily implementable. Trace-driven simulations were performed for 42 different cache configurations using benchmark programs from SPEC92 (Standard performance Evaluation Corporation) benchmark suites. Simulation results illustrate that our algorithm can provide a peak value of approximately 8.71% improvement in the miss ratio over the best performing conventional algorithm (LRU) for the selected benchmark trace files generated from SPEC programs. This translates to a savings of hundreds of thousands of misses for typical programs referencing well over 100 million addresses.

关键词： Neural networks backpropagation algorithms Neurons Cache memory High performance computing History Electronic mail Performance evaluation Fuzzy logic Resource management

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Document zone content classification for technical document images using Artificial Neural Networks and Support Vector Machines

Document zone content classification for technical document ...

引用

International Conference on the Applications of Digital Information and Web Technologies (ICADIWT)

作者： Zaidah Ibrahim Dino Isa Rajprasad Rajkumar Graham Kendall Faculty of Comp & Math. Science Universiti Teknologi MARA Malaysia School of Electrical Engineering University of Nottingham Malaysia School of Computer Science University of Nottingham UK

Artificial Neural Networks (ANN) are a classic pattern classifier and widely applicable to various problems and are relatively easy to use. Three of the most popular ANNs are Multilayer Perceptron (MLP) with backpropagation learning algorithm, Self Organizing Map (SOM) and Recurrent Neural Network (RNN). Support Vector Machines (SVM) have gained great interest in the last few years in pattern recognition. Thus, this research compares the recognition performance of text and non-text images (text, table, figure and graph) from technical document images based on the pixel intensity of various zones between BPNN, SOM, RNN and SVM. Symmetrical and non-symmetrical zoning algorithms were compared as input. 400 different datasets have been tested and the experiments indicate that SVM classification is superior to the other three classifiers. The experiments also indicate that the combination of symmetrical and non-symmetrical zoning design is better than non-symmetrical or symmetrical zoning only.

关键词： Artificial neural networks Support vector machines Support vector machine classification Recurrent neural networks Multilayer perceptrons backpropagation algorithms Organizing Pattern recognition Image recognition Text recognition

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A comparative study of fully and partially recurrent networks

A comparative study of fully and partially recurrent network...

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International Conference on Neural Networks

作者： J. Ludik W. Prins K. Meert T. Catfolis Department of Computer Science University of Stellenbosch Stellenbosch South Africa Department of Chemical Engineering Expert Systems Applications Development Group Heverlee Belgium

A number of fully and partially recurrent networks have been proposed to deal with temporally extended tasks. However, it is not yet clear which algorithms and network architectures are best suited to certain kinds of problems. In this paper we report on experimental investigations of a quantitative nature, which address this particular need by comparing fully recurrent networks using learning algorithms such as backpropagation-through-time (BPTT), batch BPTT Quickprop-through-time, and real-time recurrent learning with Elman and Jordan partially recurrent networks on four benchmark problems: detection of three consecutive zeros, nonlinear plant identification, Turing machine emulation, and real-world distillation column modelling.

关键词： backpropagation algorithms Emulation History Computer science Africa Expert systems Application software Chemical engineering Distillation equipment Recurrent neural networks

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A stable learning algorithm for recurrent neural networks

A stable learning algorithm for recurrent neural networks

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： G. Parthasarathy H. Pareek P. Ananthraj Electronics & Electrical Communication Engineering Department Indian Institute of Technology Kharagpur India Indian Institute of Technology Kharagpur India Osmania university Hyderabad India

The authors used the Liapunov approach to derive a new set of sufficient conditions that explain the stability of feedforward networks. A simplification of these conditions results in a new recurrent backpropagation algorithm. This algorithm preserves the local updating characteristic of the original algorithm but is, at the same time, found to be quite effective even for problems which offered resistance to solution by L. B. Almeida's (1987) approach.< >

关键词： Recurrent neural networks backpropagation algorithms Neural networks Multi-layer neural network Stability criteria Multilayer perceptrons Artificial intelligence Pattern classification Iterative algorithms Annealing

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Bistatic fixed-receiver parasitic SAR processor based on the back-propagation algorithm

Bistatic fixed-receiver parasitic SAR processor based on the...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： J. Sanz-Marcos P. Prats J.J. Mallorqui Signal Theory and Communications Department Universitat Politecnica de Catalunya Barcelona Spain

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