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检索条件"主题词=Backpropagation Algorithms"

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Data recovery in two-dimensional read-out of optical tape

Data recovery in two-dimensional read-out of optical tape

引用

Optical Data Storage

作者： A.D. Simone P.A. Mitkas G.A. Betzos Department of Electrical Engineering Colorado State University Fort Collins CO USA

Optical tape offers the greatest volumetric storage density of any commercially available storage medium, and thus is a cost effective alternative to traditional forms of archival storage. An optical tape recorder utilizing linear scanning with a single reel storage capacity of one terabyte has been commercially available for some time. We utilize a two-dimensional read-out based on linear scanning which provides a page oriented format similar to that of a holographic memory. Two-dimensional optical memories promise to provide high storage capacities, making them ideal candidates for archival storage in future computing applications. This extension of channel capability has the potential to increase the read bandwidth of the storage system allowing faster retrieval of a larger file space than traditional archival storage. Data recovery with image processing techniques and with an artificial neural network are discussed.

关键词： Optical network units Holography Holographic optical components Optical recording Optical computing History Neural networks Relational databases Intelligent networks backpropagation algorithms

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Learning of neural networks from linguistic knowledge and numerical data

Learning of neural networks from linguistic knowledge and nu...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： H. Ishibuchi M. Nii Department of Industrial Engineering Osaka Prefecture University Sakai Osaka Japan

This paper discusses the learning of multilayer feedforward neural networks from linguistic knowledge and numerical data. These two kinds of information are simultaneously utilized in the learning of neural networks. We show backpropagation type learning algorithms for classification problems and function approximation problems. For pattern classification problems, linguistic knowledge is represented by fuzzy if-then rules such as "If x/sub 1/ is small and x/sub 2/ is large then Class 1" and "If x/sub 1/ is large then Class 3." These fuzzy if-then rules are used in the learning of neural networks together with numerical data such as {(x/sub 1/, x/sub 2/, x/sub 3/; class label)}= {(0.1, 0.9, 0.3; Class 1),..., (0.7, 0.9, 0.8; Class 3)}. For function approximation problems, linguistic knowledge such as "If x/sub 1/ is small and x/sub 2/ is large then y is small" is utilized in the learning of neural networks together with numerical data such as {(x/sub 1/, x/sub 2/, x/sub 3/; y)}={(0.1, 0.8, 0.2; 0.2),..., (0.2, 0.3, 0.9; 0.9)}. The learning of neural networks from these two kinds of information is illustrated using computer simulations on several numerical examples. Handling of inconsistency in linguistic knowledge is discussed in this paper. Inconsistency between linguistic knowledge and numerical data is also discussed.

关键词： Neural networks Multi-layer neural network Function approximation Fuzzy neural networks Feedforward neural networks Approximation algorithms Classification algorithms backpropagation algorithms Pattern classification Computer simulation

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Selection of convergence coefficient with automata learning rule

Selection of convergence coefficient with automata learning ...

引用

International Conference on Neural Networks

作者： N.O. Ezzati K. Faez EE. Department Amirkabir University of Technology슠 Tehran Iran

In this paper an approach for selection of convergence coefficient in a backpropagation learning rule is presented. This approach uses a stochastic automata learning rule for selection of the best coefficient in each step of the learning phase. This approach is applied to a nonlinear function approximation problem. Simulation results show that it gives faster convergence than the conventional and adaptive learning rate backpropagation rules.

关键词： Convergence Learning automata Neural networks Stochastic processes Network topology backpropagation algorithms Acceleration Optimization methods Paper technology Function approximation

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Conditional market segmentation by neural networks

Conditional market segmentation by neural networks

引用

Annual Hawaii International Conference on System Sciences (HICSS)

作者： M. Natter Department of Industrial Information Processing Vienna University of Economics and Business Administration Vienna Austria

An artificial neural network (ANN) algorithm is proposed that incorporates both cluster and discriminant (or regression) analysis of the segments. The method simultaneously estimates the models relating consumer characteristics to market segments, i.e., subjects are assigned to (unique) segments so that subjects within a class show similar purchase behavior and share the same characteristics (psychographics/sociodemographics). Parameters of all models are estimated by the backpropagation algorithm. The performance of the ANN methodology is assessed in a Monte Carlo study. In contrast to the usual stepwise approach adopted in segmentation studies, our study found that simultaneous segmentation and discrimination are preferable for finding an overall optimum in that this way clusters are formed not only to create homogeneous submarkets but also to show a good discriminatory behaviour.

关键词： Neural networks Artificial neural networks Aggregates Industrial economics Information processing Clustering algorithms Algorithm design and analysis Psychology backpropagation algorithms Monte Carlo methods

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A new local linearized least squares algorithm for training feedforward neural networks

A new local linearized least squares algorithm for training ...

引用

International Conference on Neural Networks

作者： O. Stan K.W. Kamen School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta GA USA

An algorithm used to train the weights of a feedforward neural network is the global extended Kalman filter (GEKF) algorithm, which has much better performance than the popular gradient descent with error backpropagation in terms of convergence and quality of solution. However, the GEKF is very computationally intensive, and this has led to the development of simplified algorithms based on the partitioning of the global nonlinear optimization problem into a set of local nonlinear problems at the neuron level. In this paper a new training algorithm is developed by viewing the local subproblems as recursive linearized least squares problems. The objective function of the least squares problems for each neuron is the sum of the squares of the linearized backpropagated error signals. The new algorithm is shown to give better convergence results for two benchmark problems in comparison to existing local algorithms.

关键词： Least squares methods Neural networks Feedforward neural networks backpropagation algorithms Neurons Partitioning algorithms Computational complexity Nonlinear equations Multilayer perceptrons Differential equations

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The effect of reasoning with state information

The effect of reasoning with state information

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： C. LeBlanc S.I. Hruska Department of Computer Science Keene State College Keene NH USA Department of Computer Science Florida State University Tallahassee FL USA

In this article, we describe a method for the incremental development of knowledge-based neural systems which incorporate state information about a problem domain. Human expert domain knowledge is encoded in the architecture of a computational network and data-driven techniques are used to refine the encoded knowledge. State information, in the form of state nodes and recurrent network connections, is then added in order to augment the refined knowledge even further. The methodology is tested in the domain of protein secondary structure prediction, where a set of domain rules, the Chou-Fasman algorithm, is implemented in phases as an expert network. The domain rules are then refined using a backpropagation-like learning algorithm. Finally, state information is added to the resulting network and is shown to increase prediction accuracy.

关键词： Network topology Protein engineering Computer science Humans Computer networks backpropagation algorithms Feedforward systems Amino acids Educational institutions Testing

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Temporal classification of patient anaesthetic states

Temporal classification of patient anaesthetic states

引用

IEEE International Conference on Intelligent Engineering Systems (INES)

作者： L. Vefghi D.A. Linkens Intelligent Systems Laboratory Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

The goal of this study is to explore the ability of temporal neural network models to classification of patient anaesthetic states. Application of standard multilayer perceptron networks to recognise the states of anaesthesia already has produced impressive results. Encouraged by these results, we attempt to address the question of how such models can be expanded to capture some critical aspects of the dynamic nature of anaesthesia. An extension of the conventional multilayered feedforward networks to have memory for past values is undertaken to address the issue.

关键词： Biological neural networks Multilayer perceptrons Pattern classification Pain Muscles Patient monitoring Heart rate Expert systems backpropagation algorithms Intelligent systems

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Parallel neural network training on Multi-Spert

Parallel neural network training on Multi-Spert

引用

IEEE International Conference on algorithms and Architectures for Parallel Processing (ICAP)

作者： P. Farber K. Asanovic International Computer Science Institute Berkeley CA USA

Multi-Spert is a scalable parallel system built from multiple Spert-II nodes which we have constructed to speed error backpropagation neural network training for speech recognition research. We present the Multi-Spert hardware and software architecture, and describe our implementation of two alternative parallelization strategies for the backprop algorithm. We have developed detailed analytic models of the two strategies which allow us to predict performance over a range of network and machine parameters. The models' predictions are validated by measurements for a prototype five node Multi-Spert system. This prototype achieves a neural network training performance of over 530 million connection updates per second (MCUPS) while training a realistic speech application neural network. The model predicts that performance will scale to over 800 MCUPS for eight nodes.

关键词： Neural networks Predictive models Prototypes Speech recognition Hardware Software architecture Software algorithms backpropagation algorithms Performance analysis Software prototyping

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An evolutionary programming-based probabilistic neural networks construction technique

An evolutionary programming-based probabilistic neural netwo...

引用

International Conference on Neural Networks

作者： M. Sarkar B. Yegnanarayana Department of Computer Science & Engineering Indian Institute of Technology Chennai India Indian Institute of Technology Madras Chennai Tamil Nadu IN

Quick learning ability of a probabilistic neural network (PNN) has made it a popular alternative to feedforward neural networks trained by backpropagation algorithm. However in real life applications, where training set size is quite large, the PNN model suffers from a huge memory overhead and a long testing time. Moreover, its generalization capability critically depends on the choice of certain architectural parameters, which are presently chosen in an ad hoc basis. Since the PNN can be viewed as a Gaussian mixture model, the above drawbacks can be avoided if the PNN is configured based on a Gaussian mixture model with an optimum parameter set. In this paper, an evolutionary programming based clustering technique is employed to determine the optimum parameter set of the Gaussian mixture model. The efficacy of the proposed scheme is demonstrated on a Contract Bridge game opening bid problem.

关键词： Neural networks Feedforward neural networks backpropagation algorithms Genetic programming Clustering algorithms Contracts Bridges Computer science Application software Life testing

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Fuzzy modeling control for robotic gait synthesis

Fuzzy modeling control for robotic gait synthesis

引用

IEEE Conference on Decision and Control

作者： Jih-Gau Juang Department of Electrical Engineering University of Missouri Columbia MO USA

ISBN: (纸本)0780341872

This paper presents a biped locomotion learning scheme using a fuzzy modeling neural network. The learning scheme can generate walking gaits by providing a reference trajectory which defines the desired step width, height and period in several stages. This proposed scheme uses a fuzzy controller combined with a linearized inverse biped model. A multilayer fuzzy neural network is used as a controller; it provides the control signals in each stage of a walking gait. The algorithm used to train the network is the backpropagation through time. The linearized inverse biped model provides the error signals which can be used to back propagate through the controller in each stage. The simulation results are described for a five-link biped walking robot.

关键词： Fuzzy control Robot control Legged locomotion Fuzzy neural networks Inverse problems Network synthesis Neural networks Multi-layer neural network backpropagation algorithms Error correction

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