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检索条件"主题词=Backpropagation Algorithms"

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Equalization of non-linear perturbations by a multilayer perceptron in satellite channel transmission

Equalization of non-linear perturbations by a multilayer per...

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： P. Balay J. Palicot Center Commun d''Etudes en Telediffusion et Télécommunications France

Perturbations generated by the channel are among the most important limits in digital communication applications. A possibility for decreasing the intersymbol interference is via the use of equalization techniques. The satellite channel transmission is an important source of non-linear perturbations. The cancellation of this type of perturbation requires the use of a non-linear equalizer. In this context, many studies have been done using decision feedback equalizers, Volterra series, etc. Despite the improvement in the results compared to those obtained with transversal filters, these results are generally not sufficient compared to bit error ratio in the linear channel. Neural networks, thanks to their ability to create decision regions as complex as the application needs, constitute a new alternative. We show that the multilayer perceptron, with a modified backpropagation algorithm, provides, compared to transversal filters with least mean square algorithm, an interesting improvement concerning bit error rate.

关键词： Multilayer perceptrons Transversal filters Digital communication Intersymbol interference Satellites Context Decision feedback equalizers Neural networks backpropagation algorithms Least mean square algorithms

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Training techniques to obtain fault-tolerant neural networks

Training techniques to obtain fault-tolerant neural networks

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International Symposium on Fault-Tolerant Computing (FTCS)

作者： Ching-Tai Chin K. Mehrotra C.K. Mohan S. Rankat Sch. of Comput. & Inf. Sci. Syracuse Univ. NY USA School of Computer and Information Science Syracuse University New York USA Syracuse University Syracuse NY US

This paper addresses methods of improving the fault tolerance of feedforward neural nets. The first method is to coerce weights to have low magnitudes during the backpropagation training process, since fault tolerance is degraded by the use of high magnitude weights; at the same time, additional hidden nodes are added dynamically to the network to ensure that desired performance can be obtained. The second method is to add artificial faults to various components (nodes and links) of a network: during training. The third method is to repeatedly remove nodes that do not significantly affect the network: output, and then add new nodes that share the load of the more critical nodes in the network. Experimental results have shown that these methods can obtain better robustness than backpropagation training, and compare favorably with other approaches.< >

关键词： Fault tolerance Neural networks Artificial neural networks Feedforward neural networks Robustness Information science Degradation Computer networks backpropagation algorithms Neural network hardware

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Partial-discharge diagnosis with artificial neural networks

Partial-discharge diagnosis with artificial neural networks

引用

International Conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： R. Badent K. Kist N. Lewald A.J. Schwab Institute of Electric Energy Systems and High-Voltage Technology University of Karlsruhe Germany

The new diagnosis method employs a classical PD measurement system consisting of a coupling capacitor, measuring impedance, and a "wideband" integrator, cascaded by an artificial network evaluation. Upon passing a polarity detection unit, the output signal of the "wideband" integrator is recorded via a digital storage oscilloscope which simultaneously serves as an interface to the subsequent computer-aided evaluation. The personal computer stores the PD-values in a phase resolving PD-matrix. After sufficient learning with training matrices the system recognizes different fault types with high probability. The recognition likelihood of trained patterns is almost 100 percent and of a new pattern approximately 90 percent, depending on both the number of training matrices and the repetition rate. The implemented artificial neural network is composed of a three layer backpropagation algorithm with threshold units and a recognition volume of up to 16 fault types. To guarantee the highest individual detection rate, each fault type must be trained with the same number of matrices. Thereafter, the network is able to recognize previously learned fault types without any other data pre- or post-processing, i.e. the diagnosis system relies exclusively on pattern recognition.< >

关键词： Artificial neural networks Pattern recognition Impedance measurement Capacitors Oscilloscopes Computer interfaces Microcomputers Signal resolution backpropagation algorithms Fault detection

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A new training method for precision-limited analog neural networks

A new training method for precision-limited analog neural ne...

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International Joint Conference on Neural Networks (IJCNN)

作者： W. Shields Neely Chwan-Hwa Wu Department of Electrical Engineering Aubum University AL USA

Neural networks with a large number of neurons can be implemented in hardware if the precision of the weights and inputs is limited. This paper centers on situations where two conditions are present; first, that the neural network is implemented using limited precision hardware, and second, that the weights were calculated using higher precision values than can be stored in the hardware. This paper shows that limited precision weights in the target system can result in forward calculation errors that are sufficiently large to cause classification mistakes in an example problem. The cause of classification mistakes is identified in terms of topological features in weight-space. A numerical calculation, that demonstrates the existence of weight-space regions where such errors occur, is presented. The Big Valley Search algorithm is proposed to overcome the effects of limited storage. Experimental results are presented to show that the Big Valley Search algorithm can be used to overcome implementation problems encountered when implementing a fault classifier using an analog neural network chip. Using experimental results, the requirement for chip-in-loop (CIL) programming of analog chips is shown to be unnecessary.< >

关键词： Neural networks Time of arrival estimation backpropagation algorithms Artificial neural networks Neural network hardware Computer networks Analog circuits Convergence Neurons Circuit faults

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On training artificial neural networks to identify periodic functions

On training artificial neural networks to identify periodic ...

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International Joint Conference on Neural Networks (IJCNN)

作者： B.S. Behun J.J. Garside R.H. Brown Marquette University Milwaukee WI USA

Training an artificial neural network (ANN) to represent some type of plant, system, or general algebraic function is relatively straightforward and many methods exist. However, most of these methods and ANN architectures do not take into account any a priori knowledge that is often known for the problem of interest. In this paper, a priori knowledge is utilized to encourage the output of the ANN to be a periodic function of the input. Methods are investigated and compared with the general gradient-based (backpropagation) algorithm. Results show improved convergence characteristics and, in some cases, the guarantee of output periodicity with respect to the input. Generalizations can be made to higher dimensional spaces, but these may or may not be reasonable in terms of computational time and effort.< >

关键词： Artificial neural networks Neurons backpropagation algorithms Cost function Convergence Predictive models Power system modeling Natural gas Water resources Energy consumption

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A scalable bit-sequential SIMD array for nearest-neighbor classification using the city-block metric

A scalable bit-sequential SIMD array for nearest-neighbor cl...

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International Conference on Application Specific Array Processors

作者： M. Neschen Laboratoire d'Informatique Ecole Polytechnique Palaiseau France

We present a fully scalable SIMD array architecture for a most efficient implementation of pattern classification by nearest-neighbor algorithms using the city-block metric. The elementary accumulator cell is highly optimized for a sequential accumulation of absolute integer differences, so that several hundreds of them can be easily integrated on a single chip. A two-dimensional M/spl times/N array structure, reflecting an inherent two-fold data parallelism of the applications, reduces the data transfer to off-chip memory from O(M/spl times/N) to O(M+N). Here, we discuss the realization of a VLSI structure, the system architecture, and large networks of associative blocks as possible applications.< >

关键词： Pattern recognition Parallel processing Databases Prototypes Pattern classification Classification algorithms Very large scale integration Fingerprint recognition backpropagation algorithms Neural networks

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GMDP: a novel unified neuron model for multilayer feedforward neural networks

GMDP: a novel unified neuron model for multilayer feedforwar...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shengtun Li Yiwei Chen E.L. Leiss Department of Computer Science University of Houston Houston TX USA Western Atlas Software Houston TX USA

A variety of neural models, especially higher-order networks, are known to be computationally powerful for complex applications. While they have advantages over traditional multilayer perceptrons, the nonuniformity in their network structures and learning algorithms creates practical problems. Thus there is a need for a framework that unifies these various models. This paper presents a novel neuron model, called generalized multi-dendrite product (GMDP) unit. Multilayer feedforward neural networks with GMDP units are shown to be capable of realizing higher-order neural networks. The standard backpropagation learning rule is extended to this neural network. Simulation results show that single layer GMDP networks provide an efficient model for solving general problems on function approximation and pattern classification.< >

关键词： Neurons Nonhomogeneous media Multi-layer neural network Neural networks Feedforward neural networks Computer networks Multilayer perceptrons backpropagation algorithms Function approximation Pattern classification

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Speed up the learning process of feedforward neural networks

Speed up the learning process of feedforward neural networks

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International Joint Conference on Neural Networks (IJCNN)

作者： L.Y. Tseng T.H. Huang Department of Applied Mathematics National Chung Hsing University Taichung Taiwan

Among the feedforward network learning rules, the backpropagation learning rule is one that has been successfully applied to a variety of problems. However, the process of backpropagation learning is somewhat a "black box", it suffers from several limitations. We propose a scheme which uses the Hamming coding, the partitioned network, and the logic design theory to help the feedforward network to learn so as to speed up the learning process. Our experimental results reveal that the feedforward neural networks do not need to learn blindly, in fact, they can be taught to learn. The advantages of the proposed scheme are: the network size becomes smaller, the learning rate is higher, and the learning speed is faster as well.< >

关键词： Neural networks Feedforward neural networks backpropagation algorithms Convergence Multi-layer neural network Mathematics Image coding Data preprocessing Data mining Neurons

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A decentralized control architecture for nonlinear systems using multilayer feedforward neural networks

A decentralized control architecture for nonlinear systems u...

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International Joint Conference on Neural Networks (IJCNN)

作者： Young-Moon Park Myeon-Song Choi K.Y. Lee Department of Electrical Engineering Seoul National University Seoul South Korea Department of Electrical Engineering Pennsylvania State University University Park PA USA

This paper presents a decentralized control architecture with feedforward neural networks for the control problem of complex nonlinear systems. In this method, the decentralized technique was used to treat several simple subsystems instead of a full complex system in order to reduce the training time of neural networks, and the neural networks' nonlinear mapping ability is exploited to handle nonlinear interaction variables between subsystems. The decentralized control architecture is composed of local neuro-controllers, local neuro-identifiers and an overall interaction neuro-identifier. With the interaction neuro-identifier that catches interaction characteristics, a local neuro-identifier is trained to simulate a subsystem dynamics. A local neuro-controller is trained to learn how to control the subsystem by using a generalized backpropagation through time training algorithm. The proposed neural network-based decentralized regulating scheme is applied in a typical nonlinear system of two inverted pendulums connected by a spring and compared with the conventional centralized controller.< >

关键词： Distributed control Nonlinear systems Neural networks Nonlinear control systems Control systems Feedforward neural networks Nonlinear dynamical systems backpropagation algorithms Springs Centralized control

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Maximizing fault tolerance in multilayer neural networks

Maximizing fault tolerance in multilayer neural networks

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International Joint Conference on Neural Networks (IJCNN)

作者： Chun-Shin Lin Ing-Chyuan Wu Department of Electrical and Computer Engineering University of Missouri Columbia Columbia MO USA

Good fault tolerance is desired in many control and automated systems. It has been long claimed that neural networks are fault tolerant, i.e., they can continue to operate after sustaining partial damage. However, very little evidence shows that neural networks evolved from general learning algorithms really possess such merit. In this paper, the authors examine a learning method that intends to maximize the fault tolerance. The method is based on the well-known backpropagation learning algorithm. During the training, each neuron is given a small probability to have a simulated failure. This modification enforces that the computation be distributed to different computing elements in the network and thus maximizes the fault tolerance. Neurocontrollers developed using the new and conventional learning methods for pole balancing are compared.< >

关键词： Multi-layer neural network Fault tolerance Neural networks backpropagation algorithms Learning systems Computer networks Distributed computing Fault tolerant systems Automatic control Control systems

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