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检索条件"主题词=Backpropagation Algorithms"

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Neural engineering in management application

Neural engineering in management application

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Zhonglin Zhou Tao li Yongqin Xu J. Bode Shouju Ren Institute of Microelectronics Department of Automation TsingHua University Beijing China

Describes the concept of neural engineering which supports decision-making in the systematic construction of neural networks. We gave some guidelines on building neural networks tailored to a specific applications, such as: what is the optimal number of hidden nodes, how to prune the input nodes, segregation and transformation of data etc. Experiments were carried on cost estimation using a multi-layer perceptron (backpropagation algorithm).

关键词： Neural engineering Engineering management Neural networks Cost function Automation Guidelines Buildings backpropagation algorithms Manufacturing Artificial neural networks

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Neural Networks for the Design and Reverse Engineering of BJTs

Neural Networks for the Design and Reverse Engineering of BJ...

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European Conference on Solid-State Device Research (ESSDERC)

作者： Ryan Ferguson David J. Roulston Department of Electrical and Computer Engineering University of Waterloo Waterloo ONT Canada

We have developed a neural network based software tool, REED (Rapid Engineering of Electron Devices) that maps an electrical description of a bipolar transistor to its physical structure, as described by mask geometri... 详细信息

关键词： Neural networks Reverse engineering SPICE Doping profiles backpropagation algorithms Semiconductor process modeling Software tools Design engineering Electrons Bipolar transistors

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Local minima and generalization

Local minima and generalization

引用

International Conference on Neural Networks

作者： S. Lawrence Ah Chung Tsoi C.L. Giles Electrical and Computer Engineering University of Queensland QLD Australia NEC Research Institute Inc. Princeton NJ USA Institute for Advanced Computer Studies University of Maryland College Park MD USA

We consider a number of popular beliefs within the neural network community on the training and generalization behavior of multilayer perceptrons, and to some extent recurrent networks that: 1) the solution found is often close to the global minimum in terms of the magnitude of the error; 2) smaller networks generalize better than larger networks; and 3) the number of parameters in the network should be less than the number of data points in order to provide good generalization. For the tasks and methodology we consider, we show that: 1) the solution found is often significantly worse than the global minimum; 2) oversize networks can provide improved generalization due to their ability to find better solutions; and 3) the optimal number of parameters with respect to generalization error can be much larger than the number of data points.

关键词： Neurons National electric code Neural networks Multilayer perceptrons Interference backpropagation algorithms Computer networks Data engineering Australia Multi-layer neural network

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Design of adaptive NNs-robust-PID controller for a robot control

Design of adaptive NNs-robust-PID controller for a robot con...

引用

IEEE International Symposium on Intelligent Control (ISIC)

作者： S. Yildirim M.F. Sukkar R. Demirci V. Aslantas Engineering Faculty Erciyes University Robotic Research Laboratory Kayseri Turkey Automatic Control and Industrial Electronics Department Electrical and Electronics Engineering Faculty Aleppo University Aleppo Syria Electrical Department Technical Education Faculty Abant Izzet Baysal University Duzce Bolu Turkey

This paper investigates the trajectory control of a robot using a new type of recurrent neural network. A three-layered recurrent neural network is used to estimate the forward dynamics model of the robot manipulator. The standard backpropagation (BP) algorithm is used as a learning algorithm for this network to minimise the difference between the robot manipulator actual response and that predicted by the neural network. This algorithm is employed to update the connection weights of a recurrent neural network controller with three layers using a stochastic gradient function. The control architecture consists of a neural feed-forward model which is a recurrent network used for identification of the robot dynamics, a conventional PID controller, a robust controller and a neural controller. Simulations illustrate that the proposed neural control approach which is applied to some nonlinear processes can gain satisfactory performance results. The results of the simulations are presented to show the promising performance of the neural controller.

关键词： Robot control Programmable control Adaptive control Recurrent neural networks Manipulator dynamics backpropagation algorithms Neural networks Stochastic processes Feedforward systems Three-term control

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Real time system modelling using locally recurrent neural networks

Real time system modelling using locally recurrent neural ne...

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Mediterranean Electrotechnical Conference (MELECON)

作者： P. Campolucci A. Uncini F. Piazza Dipartimento di Elettronica c Autornatica University di Ancona Ancona Italy

In this paper dynamic neural networks for system modelling are considered: architectural issues are presented but the paper focuses on learning algorithms that work real-time. A recent architecture called locally recurrent neural network is presented in its different versions and compared to traditional networks internally static but provided with external buffer and MLP with finite memory synapses. Simulations results show better modelling performance for locally recurrent networks and so an improved training algorithm is developed for them: causal backpropagation through time. Validation tests shows that the networks are modelling the underlying system and not just overfitting the data.

关键词： Recurrent neural networks Neural networks Nonlinear dynamical systems Neurofeedback Output feedback backpropagation algorithms Finite impulse response filter Neurons Electronic mail Computer hacking

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SHAKE-A multi-criterion optimization scheme for neural network training

SHAKE-A multi-criterion optimization scheme for neural netwo...

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International Conference on Neural Networks

作者： S.S. Pacheco A.G. Thome Computer Department Military Institute of Engineering Rio de Janeiro Brazil Electrical Engineering Department Military Institute of Engineering Rio de Janeiro Brazil

This paper presents a new approach for the task of feedforward type neural network training process based on a multi-criterion efficiency measurement. Here we propose a novel hybrid neuro-genetic algorithm that tries to optimize a three dimension criterion vector composed by speed, accuracy, and percentage of convergence, which measures the overall stability of the training algorithm to converge to good minimal. The proposed approach takes the speed advantage of the conventional algorithms as well as the accuracy and percentage of convergence advantages of the genetic algorithms. The empirical results obtained up to now shows the strength and potentiality of the method.

关键词： Neural networks Pareto optimization Velocity measurement Accidents Military computing Convergence backpropagation algorithms Cost function Genetic algorithms Job production systems

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Modular neural networks for friction modeling and compensation

Modular neural networks for friction modeling and compensati...

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IEEE Conference on Control Technology and Applications (CCTA)

作者： Meng-Hock Fun M.T. Hagan School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

The modular neural network has been shown to be an effective alternative to multilayer feedforward networks, especially for implementing functions with sharp changes. The objective of this work is to use modular neural networks to model precision pointing systems whose performance is limited by nonlinear friction forces. The modular neural network models are used to develop friction compensation controllers. This paper also describes a new method for training modular networks, based on the Levenberg-Marquardt algorithm for nonlinear least squares. The algorithm is tested on several function approximation problems, and the performance is compared with standard steepest ascent and the Rprop algorithm.

关键词： Neural networks Friction Performance analysis backpropagation algorithms Transfer functions Nonlinear equations Newton method Computer networks

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Noise robustness enhancement using fourth-order cumulants cost function

Noise robustness enhancement using fourth-order cumulants co...

引用

International Conference on Neural Networks

作者： C.T. Leung T.W.S. Chow Department of Electronic Engineering City University of Hong Kong Kowloon Hong Kong China

A novel robust fourth-order cumulants cost function is introduced to enhance the fitting to underlying function in small data sets with high noise level of Gaussian noise. The neural network learns based on the gradient descent optimization method by introducing a constraint term in the cost function. The proposed cost function was applied to benchmark sunspot series prediction and nonlinear system identification. Excellent results are obtained. The neural network can provide lower training error and excellent generalization property. Our proposed cost function enables the network to provide, at most, 73% reduction of normalized test error in the benchmark test.

关键词： Noise robustness Cost function Neural networks backpropagation algorithms Gaussian noise Benchmark testing Least squares methods Function approximation Data engineering Noise level

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Identification and control of induction motors using artificial neural networks with random weight change training

Identification and control of induction motors using artific...

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AFRICON Conference

作者： B. Burton R.G. Harley T.G. Habetler Department of Electrical-Engineering University of Natal Durban South Africa

This paper highlights some of the latest developments in ongoing work on the practical implementation of a continually online trained neural network induction motor controller. In particular, it considers the potential use of a proposed analogue VLSI sigmoidal feedforward neural network, with on-chip random weight change training, for rapid control of induction motor stator currents. The random weight change training algorithm is described and its convergence mechanism explained by comparison with that of backpropagation training. Simulation results also demonstrate the potential induction motor current control performance achievable with random weight change in comparison with that of backpropagation training.

关键词： Induction motors Artificial neural networks Very large scale integration Neural networks Feedforward neural networks Network-on-a-chip Stators backpropagation algorithms Convergence Current control

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Three connectionist implementations of dynamic programming for optimal control: a preliminary comparative analysis

Three connectionist implementations of dynamic programming f...

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International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing

作者： H. Bersini V. Gorrini IRIDIA-Universite Libre de Bruxelles Brussels Belgium

Three optimal control methodologies all relying on neural network for their universal approximation capabilities and on dynamic programming for substituting the time-integral optimization by a succession of time-local optimizations are presented in this paper and applied on the same elementary rendezvous problem. First a simplified version of the backpropagation-through-time algorithm is presented as the most faithful implementation of dynamic programming when the optimal controller is approximated by a neural network (learning by gradient descent) and the process model is available. Relaxing the need for an explicit prior modelling of the process model, reinforcement learning (RL) approaches, both for continuous and discrete controllers, are described and tested on the rendezvous problem. The results and the numerous methodological difficulties we met are discussed. The most successful reinforcement learning is the connectionist implementation of Q-learning with all Q-values approximated by radial-basis-function networks. However when searching for a continuous optimal controller, the price RL has to pay for the absence of model turns out to be far from negligible in terms of methodological difficulties, lack of robustness, convergence time and quality of the discovered solution.

关键词： Dynamic programming Optimal control backpropagation algorithms Learning Neural networks Optimization methods Cost function Delay effects Jacobian matrices Lagrangian functions

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