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neural network based routing in computer communication networks

Neural network based routing in computer communication netwo...

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IEEE International conference on Systems Engineering

作者： Y.C. Ouyang A.A. Bhatti Department of Electrical Engineering Memphis State University Memphis TN USA

A neural-network-based routing algorithm is presented which demonstrates the ability to take into account simultaneously the shortest path and the channel capacity in computer communication networks. A Hopfield-type of neural-network architecture is proposed to provide the necessary connections and weights, and it is considered as a massively parallel distributed processing system with the ability to reconfigure a route through dynamic learning. This provides an optimum transmission path from the source node to the destination node. The traffic conditions measured throughout the system have been investigated. No congestion occurs in this network because it adjusts to the changes in the status of weights and provides a dynamic response according to the input traffic load. Simulation of a ten-node communication network shows not only the efficiency but also the capability of generating a route if broken links occur or the channels are saturated

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neural networks for data fusion

Neural networks for data fusion

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IEEE International conference on Systems Engineering

作者： S.P. Chaudhuri S. Das Sensor Data Integration Inc. Concord MA USA

The application of artificial neural network technology to data fusion for target recognition is discussed. The specific application includes airborne target recognition primarily using information available from radar and EO-IR thermal imaging sensors. The artificial neural network based fusion architectures from alternative approaches are discussed, such as alternative learning algorithms, a training set, and massively parallel distributed processing for making real-time automatic target recognition decisions. The experimental results indicate that target recognition performance can be greatly enhanced by using an array of complementary sensors whose outputs are fused to extract information not otherwise available from a single sensor

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The self organizing neural network algorithm: adapting structure for optimum supervised learning

The self organizing neural network algorithm: adapting struc...

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Annual Hawaii International conference on System Sciences (HICSS)

作者： M.F. da M. Tenorio Parallel Distributed Structures Laboratory School of Electrical Engineering Purdue University West Lafayette IN USA

An algorithm called the self-organizing neural network (SONN) is described, and its use as a supervised learning architecture is demonstrated. The algorithm constructs a network, chooses the neuron functions, and adjusts the weights. The final network structure is optimal in the sense that it uses simulated annealing in the model search. The results (number of weights, complexity of the final structure, computer time, and model accuracy) are compared to the back-propagation algorithm. They show that SONN constructs a simpler, more accurate model, requiring fewer training data and epochs.< >

关键词： Organizing neural networks Supervised learning Neurons Transfer functions Parallel processing Taxonomy Laboratories Intelligent networks Computer architecture

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Possibility of neural networks controller for robot manipulators

Possibility of neural networks controller for robot manipula...

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IEEE International conference on Robotics and Automation (ICRA)

作者： T. Yabuta T. Yamada NTT Transmission Systems Laboratories Tokai Ibaraki Japan

NN (neural network) controller characteristics are clarified by comparison with the adaptive control theory. The authors explain the classification of the NN controller architecture and the dynamic NN structure. A comparison between the NN controller and the adaptive controller shows that the framework of a linear two-layer NN controller is the same as that of the adaptive controller, and that the nonlinear three-layer NN (PDP, or parallel distributed processing type) is a nonlinear extension of the adaptive control. The stability characteristics of the NN control system, which shows the robustness effect of the generalized delta rule, the plant and the NN mapping function, are treated. Finally, NN controller experiments are demonstrated using a force control servomechanism. Experimental results suggest that the nonlinear sigmoid function of the NN can compensate for the nonlinear plant effect.< >

关键词： neural networks Robot control Adaptive control Programmable control Robust stability Force control Manipulator dynamics distributed processing Control systems Robust control

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A neural Net Approach to Space Vehicle Guidance

A Neural Net Approach to Space Vehicle Guidance

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American Control conference (ACC)

作者： Alper K. Caglayna Scott M. Allen Charles River Analytics Inc. 55 Wheeler Street Cambridge MA. Telephone: (617) 491-3474 Charles River Analytics 슠Inc. Cambridge MA USA

The on-line implementation of numerical algorithms for solving the optimum trajectory/guidance problem for advanced space vehicles such as ALS, HLLV, AOTV, transatmospheric vehicles and interplanetary spacecraft is not possible due to their complexity. Hence, the current approach to the development of real-time guidance laws for these advanced space vehicles is to use approximation theory to obtain closed-loop guidance laws. neural networks offer an alternative to the derivation and implementation of guidance laws. In this paper, we formulate the space vehicle guidance problem using a neural network approach and investigate the appropriate neural net architecture for modelling optimum guidance trajectories. In particular, we investigate the incorporation of a priori knowledge about the characteristics of the optimal guidance solution into the neural network architecture. The online classification performance of the developed network is demonstrated using a synthesized network trained with a data base of optimum guidance trajectories. Such a neural network based guidance approach can readily adapt to environment uncertainties such as those encountered by an AOTV during atmospheric maneuvers.

关键词： neural networks Space vehicles Navigation Tellurium Rivers Telephony Intelligent vehicles Databases distributed processing Vehicle dynamics

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distributed recursive structure processing 90

Distributed recursive structure processing

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Proceedings of the 4th International conference on neural Information processing Systems

作者： Géraldine Legendre Yoshiro Miyata Paul Smolensky Department of Linguistics University of Colorado Boulder CO Optoelectronic Computing Systems Center University of Colorado Boulder CO Department of Computer Science University of Colorado Boulder CO

ISBN: (纸本)9781558601840

Harmonic grammar (Legendre, et al., 1990) is a connectionist theory of linguistic well-formedness based on the assumption that the well-formedness of a sentence can be measured by the harmony (negative energy) of the corresponding connectionist state. Assuming a lower-level connectionist network that obeys a few general connectionist principles but is otherwise unspecified, we construct a higher-level network with an equivalent harmony function that captures the most linguistically relevant global aspects of the lower level network. In this paper, we extend the tensor product representation (Smolensky 1990) to fully recursive representations of recursively structured objects like sentences in the lower-level network. We show theoretically and with an example the power of the new technique for parallel distributed structure processing.

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2nd Australian Joint Artificial Intelligence conference, AI 1988

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2nd Australian Joint Artificial Intelligence conference, AI 1988

ISBN: (纸本)9783540520627

The proceedings contain 32 papers. The special focus in this conference is on Artificial Intelligence. The topics include: A perspective on the nature of artificial intelligence: Enabling and enhancing capabilities for society;student modelling in a keyboard scale tutoring system;contradictions and revisions as explanatory aids in the delivery of technical information;a case study in deterministic prolog;exploring the epistemic labyrinth: New directions in the formal theory of knowledge representation;a temporal relational calculus;counterfactuals, cotenability and consistency;a knowledge acquisition tool for decision support systems;adaptive data stores;techniques for efficient empirical induction;representing exceptions in rule-based systems;what can massively parallel architectures bring to AI?;conceptual graphs from a knowledge systems viewpoint;implementing second generation rule-based financial applications today;knowledge in context: A strategy for expert system maintenance;integrating knowledge acquisition and performance systems;a machine vision system with learning capabilities;range from out of focus blur;environment mapping with a mobile robot using sonar;incorporating knowledge via regularization theory: Applications in vision and image processing;common-sense resolution of syntactic ambiguity in database queries;capability based natural language understanding;herbicide advisory systems: Weeds in wheat and other crops;character pattern recognition on a computational neural network;distributed planning and control for manufacturing operations;parallelism in nonmonotonic multiple inheritance systems;a real-time knowledge-based system for frequency management in communications;experiences in developing an intelligent operator guidance system;combining heuristics and simulation models: An expert system for the optimal management of pigs.

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Maximum likelihood density estimation by means of a PDP network

Maximum likelihood density estimation by means of a PDP netw...

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IEEE Region 10 International conference TENCON

作者： J. Wu C. Chan Department of Computer Science University of Hong Kong Hong Kong China

Based on the principle of maximizing the likelihood of proper classification of training samples, an algorithm is proposed to train the artificial neural pattern density estimator (parallel distributed processing (PDP) network) introduced by the authors earlier (1990). The previous restrictions on unit functions were relaxed such that each unit in the network represented a joint density of independent Gaussian variables with equal variances while variances across densities did not have to be the same. The algorithm was tested with samples derived from known mixtures of memoryless Gaussian sources as well as exponential and Gamma densities. Both one- and two-dimensional cases were explored. The success of the network in estimating the probability density functions depended on how well they were represented by the training samples, the number of hidden units employed and how thoroughly the network was trained. The results of comparing the network's recognition rates against those of a Bayes classifier are presented.< >

关键词： Maximum likelihood estimation Artificial neural networks Proposals Equations

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neural networks, maximum mutual information training, and maximum likelihood training (speech recognition)

Neural networks, maximum mutual information training, and ma...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： L.T. Niles H.F. Silverman M.A. Bush LEMS Division of Engineering Brown University Providence RI USA Xerox Palo Alto Research Center Palo Alto CA USA

A Gaussian-model classifier trained by maximum mutual information estimation (MMIE) is compared to one trained by maximum-likelihood estimation (MLE) and to an artificial neural network (ANN) on several classification tasks. Similarity of MMIE and ANN results for uniformly distributed data confirm that the ANN is better than the MLE in some cases due to the ANNs use of an error-correcting training algorithm. When the probability model fits the data well, MLE is better than MMIE if the training data are limited, but they are equal if there are enough data. When the model is a poor fit, MMIE is better than MLE. Training dynamics of MMIE and ANN are shown to be similar under certain assumptions. MMIE seems more susceptible to overtraining and computational difficulties than the ANN. Overall, ANN is the most robust of the classifiers.< >

关键词： neural networks Mutual information Artificial neural networks Maximum likelihood estimation Gaussian processes Training data Robustness

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RAP: a ring array processor for multilayer perceptron applications

RAP: a ring array processor for multilayer perceptron applic...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： N. Morgan J. Beck E. Allman J. Beer International Computer Science Institute Berkeley CA USA

A ring array processor (RAP) designed for fast simulation of artificial neural network algorithms is described. The RAP is a multiprocessor system which is particularly targeted in the training of feedforward networks for the recognition of continuous speech. The overall system consists of several four-processor boards serving together as an array processor for a 68020-based host running a real-time operating system. The prototype design includes 64 Mbytes of dynamic memory (expandable to 256) and 4 Mbytes of fast static RAM distributed between 16 processors on four boards. Theoretical peak performance is 512 MFLOPS, and simulations have indicated a sustained throughput of roughly half of this for algorithms of current interest, including communication overhead of roughly 10-30%. Initial tests with the new hardware have confirmed the major assumptions of the estimates from the simulations.< >

关键词： Multilayer perceptrons Process design Algorithm design and analysis Artificial neural networks Multiprocessing systems Speech recognition Target recognition Real time systems Operating systems Prototypes

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