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Mediterranean Electrotechnical Conference (MELECON)

作者： S. Saksida F. Bremsak Faculty of Electrical and Computer Engineering University of Ljubljana Ljubljana Slovenia

A modified backpropagation algorithm with a linear output function is discussed from the viewpoint of its convenience in control tasks. Two methods are presented for teaching neural networks to act as an inverse plant. In the simulation example a discrete integrator was used as the plant. The best results were obtained by a combination of both methods.< >

关键词： Neural networks Intelligent networks Control systems Neurons Artificial neural networks Pattern recognition Transfer functions backpropagation algorithms Signal generators H infinity control

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A neural network approach for the control of a tracking behavior

A neural network approach for the control of a tracking beha...

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International Conference on Advanced Robotics (ICAR)

作者： K. Berns R. Dillmann Gruppe Interaktive Planungstechnik Forschungszentrum Informatik an der Universität Karlsruhe Karlsruhe Germany

The problem of correctly evaluating noisy and incorrect data for the interpretation of ultrasonic sensor signals is an often encountered one. Neural networks, with their inherent characteristics of adaptivity and high fault- and noise-tolerance are well suited for such tasks. In this paper two neural network approaches are described for the control of the tracking behavior of an autonomous mobile robot. Input data are provided by a set of ultrasonic sensors mounted at the front of the vehicle. Two neural network learning strategies: backpropagation and reinforcement learning, are examined in a simulation and compared with respect to learning speed, capacity of tracking and the effort required to adapt the control networks of the real vehicle.< >

关键词： Neural networks Sensor phenomena and characterization Mobile robots Remotely operated vehicles Learning Ultrasonic variables measurement Sensor systems backpropagation algorithms Testing Telephony

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On using an artificial feedforward neural network as a controller in large-scale power systems

On using an artificial feedforward neural network as a contr...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： R.J. Thomas E. Sakk School of Electrical Engineering Cornell University Ithaca NY USA

The authors examine the usefulness of the feedforward neural network as a controller. In particular, the authors consider as an example the problem of design of a power system stabilizer that has the structure of a classical feedforward artificial neural network. In this case, the controller is developed for a class of problems associated with linear plants. Certain observations are made regarding the generic behavior of systems with this type of controller structure. The authors incorporate a robustness criterion into the design as well as a conventional dynamic performance criterion.< >

关键词： Artificial neural networks Neural networks Feedforward neural networks Power system control Control systems Intelligent networks Large-scale systems Power systems backpropagation algorithms Vectors

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Efficient gradient computation for continuous and discrete time-dependent neural networks

Efficient gradient computation for continuous and discrete t...

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International Joint Conference on Neural Networks (IJCNN)

作者： S. Miesbach Corporate Research and Development Center Siemens AG Munchen Germany Mathematisches Institut Technische Universität München Munich Germany

The author provides calculus-of-variations techniques for the construction of backpropagation-through-time (BTT) algorithms for arbitrary time-dependent recurrent neural networks with both continuous and discrete dynamics. The backpropagated error signals are essentially Lagrange multipliers. The techniques are easy to handle because they can be embedded into the Hamiltonian formalism widely used in optimal control theory. Three examples of important extensions to the standard BTT-algorithm provide proof of the power of the method. An implementation of the BTT-algorithms which overcomes the storage drawbacks is suggested.< >

关键词： Computer networks Neural networks Neurons Recurrent neural networks backpropagation algorithms Performance analysis Adaptive control High performance computing Planning Programmable control

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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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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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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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A high performance digital processor for implementing large artificial neural networks

A high performance digital processor for implementing large ...

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Custom Integrated Circuits Conference (CICC)

作者： D.A. Orrey D.J. Myers J.M. Vincent British Telecommunications Research Laboratories UK

A CMOS integrated circuit is described which is capable of implementing very large digital neural networks of the multilayer perceptron (MLP) form. It incorporates on-chip training using the backpropagation algorithm, and the use of pseudorandom noise allows training with coarsely quantized weight values. Dynamic range and precision of the connection weights are automatically adjusted during training, thus allowing the circuit to adapt to different network sizes and topologies. The addition of a small, pseudorandom noise element allows the weights memory to be used more efficiently for large networks. Extensive simulation using a hardware description model over a range of problems has given a high degree of confidence in the design.< >

关键词： CMOS integrated circuits Neural networks Multi-layer neural network Multilayer perceptrons backpropagation algorithms Integrated circuit noise Dynamic range Network topology Circuit topology Circuit noise

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ADAPTIVE EQUALIZATION USING THE backpropagation ALGORITHM

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1990年第6期37卷 848-849页

作者： ZHANG, QS Department of Telecommunication Theory Royal Institute of Technology Stockholm Sweden

A novel adaptive channel equalizer based on the backpropagation algorithm applied to an associative network is presented. simulations are made for linear and nonlinear channels,. The performance is shown to be much be... 详细信息

关键词： nonlinear channel backpropagation algorithms Least mean square algorithm adaptive equalization

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DATA-COMPRESSION OF THE ECG USING NEURAL NETWORK FOR DIGITAL HOLTER MONITOR

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IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 1990年第3期9卷 53-57页

作者： IWATA, A NAGASAKA, Y SUZUMURA, N Department of Electrical and Computer Engineering Nagoya Institute of Technology Japan

A data-compression algorithm for digital Holter recording using artificial neural networks (ANNs) is described. A three-layer ANN that has a hidden layer with a few units is used to extract features of the ECG (electrocardiogram) waveform as a function of the activation levels of the hidden layer units. The number of output and input units is the same. The backpropagation algorithm is used for learning. The network is tuned with supervised signals that are the same as the input signals. One network (network 1) is used for data compression and another (network 2) is used for learning with current signals. Once the network is tuned, the common waveform features are encoded by the interconnecting weights of the network. The activation levels of the hidden units then express the respective features of the waveforms for each consecutive heartbeat.< >

关键词： Data compression Electrocardiography Neural networks Monitoring Artificial neural networks Heart beat Electrodes Thorax Digital signal processors backpropagation algorithms

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