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检索条件"主题词=Backpropagation Algorithms"

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Pruning Boltzmann networks and hidden Markov models

Pruning Boltzmann networks and hidden Markov models

引用

Asilomar Conference on Signals, Systems & Computers

作者： M.W. Pedersen D.G. Stork Section for Digital Signal Processing Department of Mathematical Modelling Technical University of Denmark Lyngby Denmark Machine Learning and Perception Group Ricoh California Research Center Menlo Park CA USA

We present sensitivity-based pruning algorithms for general Boltzmann networks. Central to our methods is the efficient calculation of a second-order approximation to the true weight saliencies in a cross-entropy error. Building upon previous work which shows a formal correspondence between linear Boltzmann chains and hidden Markov models (HMMs), we argue that our method can be applied to HMMs as well. We illustrate pruning on Boltzmann zippers, which are equivalent to two HMMs with cross-connection links. We verify that our second-order approximation preserves the rank ordering of weight saliencies and thus the proper weight is pruned at each pruning step. In all our experiments in small problems, pruning reduces the generalization error; in most cases the pruned networks facilitate interpretation as well.

关键词： Hidden Markov models Signal processing algorithms Digital signal processing Mathematical model Machine learning Machine learning algorithms Pattern classification Surges Risk management backpropagation algorithms

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Adaptive two-dimensional neuron grids

Adaptive two-dimensional neuron grids

引用

International Conference on Microelectronics for Neural Networks

作者： A. Kronig U. Ramacher Institute of Circuits and Systems Department of Electrical Engineering Dresden University of Technology Dresden Germany

In the last decade many early-vision tasks have been cast into the form of global optimization principles: their solution is obtained by the minimization of appropriate cost functions. The minimization procedure, which consists in most cases of a simple gradient descent, often yields a two-dimensional particle model with local exchange interaction. Our starting point is a quite general representative of such a model, a two-dimensional neuron grid, which is based on a standard neuron model. The optimization principles enter our model via a backpropagation like adaption scheme for the weights. In the case of edge detection the results we arrive at so far are similar to those obtained by the gradient descent methods. So the formalism proposed here may form an alternative basis for more sophisticated image preprocessing algorithms.

关键词： Neurons Cost function Equations Circuits and systems Minimization methods Elementary particle exchange interactions Image edge detection Approximation algorithms backpropagation algorithms Microscopy

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The Effect Of Noise On The Generalisation & Classification Capabilities Of Simple Image Recognition Artificial Neural Networks

The Effect Of Noise On The Generalisation & Classification C...

引用

International Symposium on Signal Processing and Its Applications (ISSPA)

作者： R. Slaviero Signal Processing Associates Private Limited Wantirna VIC Australia

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Performance evaluation of neural network algorithms for multisensor data fusion in an airborne track while scan radar

Performance evaluation of neural network algorithms for mult...

引用

International Conference on Neural Networks

作者： L.M. Patnaik H. Nair V. Abraham G. Raghavendra S.K. Singh R. Srinivasan K. Ramchand Microprocessor Applications Laboratory Indian Institute of Science Bangalore India Indian Institute of Science Bangalore Karnataka IN Center for Airborne Systems DRDO Bangalore India

This paper deals with the solution to the problem of multisensor data fusion for a single target scenario as detected by an airborne track-while-scan radar. The details of a neural network implementation, various training algorithms based on standard backpropagation, and the results of training and testing the neural network are presented. The promising capabilities of RPROP algorithm for multisensor data fusion for various parameters are shown in comparison to other adaptive techniques.

关键词： Neural networks Intelligent networks Radar tracking Sensor fusion Airborne radar Azimuth backpropagation algorithms Aircraft Target tracking Radar applications

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Casasent network density estimation

Casasent network density estimation

引用

International Conference on Neural Networks

作者： A. Sarajedini P.M. Chau Department of Electrical and Computer Engineering University of California La Jolla CA USA

Density estimation is an integral component of many signal processing tasks such as pattern recognition, detection, and cluster analysis. The kernel density estimator's utility is limited in high dimensional spaces by the curse of dimensionality. Modha (1994) proposed a neural network density estimator that avoids this 'curse', but can potentially require many hidden units (and therefore a lot of computation) for even simple densities such as Gaussians. We propose an extension of Modha's neural net using higher order hidden units that allow simple densities to be modeled economically (as kernel estimators do), while retaining the flexibility to avoid the curse of dimensionality (as Modha's network does).

关键词： Neural networks Gaussian processes Kernel Transfer functions backpropagation algorithms Multilayer perceptrons Signal processing Pattern recognition Pattern analysis Signal analysis

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Learning and structural design of feedforward neural networks by employing genetic algorithms

Learning and structural design of feedforward neural network...

引用

SICE Annual Conference

作者： A.R. Bohari N. Mizuno Department of Mechanical Engineering Nagoya Institute of Technology Nagoya Japan

This work is an attempt to face learning and structural design problems simultaneously for feedforward neural networks by employing genetic algorithms and hybrid algorithm. In the learning process, the disadvantages of backpropagation as a learning algorithm can be avoided by using hybrid algorithm. On the other hand the ability of genetic algorithms to perform global search intelligently make this method as a robust learning algorithm, while in the same time design the structure. The proposed algorithm shows good performances where all of the trials of learning processes converge to the desired condition and most of structural design end with desired efficient structure.

关键词： Algorithm design and analysis Neural networks Feedforward neural networks Genetic algorithms Convergence backpropagation algorithms Adaptive algorithm Robustness Space exploration Mechanical engineering

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Extracting rules from a GA-pruned neural network

Extracting rules from a GA-pruned neural network

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhaohui Zhang Yuanhui Zhou Yuchang Lu Bo Zhang National Laboratory of Intelligent Technology & Systems Department of Computer Science Tsinghua University Beijing China

ISBN: (纸本)0780332806

Although artificial neural networks have been proven to be a powerful and general technique, they are often regarded as "black box" and the result of a network may not be easily applied to related problems. In this paper, a genetic algorithm is used to prune a trained network, then the pruned network is converted to M trees where M is the number of the output units and equal to the number of classes of the problem. Finally, rule sets are extracted for each class by analyzing each tree. In this way, we managed to utilize the advantages of artificial neural networks, genetic algorithms and symbolic learning, and avoid some of their disadvantages at the same time. The rules are shown to be interpretable while preserving the accuracy of the networks and can be easily used in related fields.

关键词： Neural networks backpropagation algorithms Artificial neural networks Network topology Feedforward neural networks Genetic algorithms Laboratories Intelligent systems Artificial intelligence Intelligent networks

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Adaptive real-time tracking controller for induction motor drives using neural designs

Adaptive real-time tracking controller for induction motor d...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： A. Rubaai D.M. Kankam Howard University College of Medicine Washington D.C. DC USA NASA Lewis Research Center Cleveland OH USA

This paper presents a learning architecture for the identification and control of nonlinear induction motor dynamics with unknown parameters. The control and identification parameters are adjusted simultaneously in real-time using the dynamic backpropagation algorithm. Both identification and control are carried out at pre-specified (and possibly different) time intervals, as the system is in operation. The proposed architecture adapts and generalizes its learning to a wide variety of loads, and in addition provides the necessary abstraction when measurements are contaminated with noise. Extensive simulations reveal that neural designs are effective means of system identification and control for time-varying nonlinear systems, in the presence of uncertainty. The difficulties addressed by this article include incomplete system knowledge, nonlinearity, noise and delays.

关键词： Programmable control Adaptive control Induction motor drives Nonlinear dynamical systems Induction motors backpropagation algorithms Control systems Noise measurement Pollution measurement System identification

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Causal back propagation through time for locally recurrent neural networks

Causal back propagation through time for locally recurrent n...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： P. Campolucci A. Uncini F. Piazza Dipartimento di Elettronica e Automatica University of Ancona Ancona Italy

This paper concerns dynamic neural networks for signal processing: architectural issues are considered but the paper focuses on learning algorithms that work on-line. Locally recurrent neural networks, namely MLP with IIR synapses and generalization of Local Feedback Multi-Layered Networks (LF MLN), are compared to more traditional neural networks, i.e. static MLP with input and/or output buffer (TDNN), FIR MLP and fully recurrent neural networks: simulations results are provided to compare locally recurrent neural networks with TDNN and FIR MLP. Moreover, we propose a new learning algorithm, based on the Back Propagation Through Time and called Causal Back Propagation Through Time (CBPTT), that is faster and more stable than the algorithm previously used for IIR MLP. The algorithm that we propose includes as particular cases the following algorithms: Wan's Temporal Back Propagation, Back Propagation for Sequences (BPS) and Back-Tsoi algorithm.

关键词： Recurrent neural networks Signal processing algorithms Neural networks backpropagation algorithms Output feedback Neurofeedback Finite impulse response filter Digital signal processing Neurons System identification

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Dynamically deactivating hidden neurons in a multilayer perceptron neural network

Dynamically deactivating hidden neurons in a multilayer perc...

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

作者： H. Amin K.M. Curtis B.R. Hayes-Gill Department of Electrical & Electronic Engineering University of Nottingham Nottingham UK

In this paper we present an approach that terminates the processing of hidden nodes, within a multilayer perceptron (MLP) neural network, if they become inactive during the learning process. The determination of the activity and non-activity of hidden nodes are based on the mean deviation of changes in the average derivative of the hidden nodes within an interval of several iterations. A decreasing threshold value is used to evaluate the mean deviation and hence to deactivate the hidden nodes accordingly.

关键词： Neurons Intelligent networks Multilayer perceptrons Neural networks Multi-layer neural network backpropagation algorithms Iterative algorithms Nonlinear equations Mean square error methods Monitoring

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