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检索条件"主题词=Backpropagation Algorithms"

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Nonlinear blind signal separation: an RBF-based network approach

Nonlinear blind signal separation: an RBF-based network appr...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Ying Tan E.E.I. Institute of Intelligent Information Science Hefei China

This paper presents a radial basis function (RBF) based approach for blind signal separation in a nonlinear mixture. A cost function, which consists of the mutual information and partial moments of the outputs of the separation system, is defined to extract the independent signals from their nonlinear mixtures. The minimization of the cost function results in the independence of the outputs with desirable moments such that the original sources are separated properly. A learning algorithm for the parametric RBF network is established by using the stochastic gradient descent method. This approach is characterized by high learning convergence rate of weights, modular structure, as well as feasible hardware implementation. A simulation result demonstrates the feasibility, and validity of the proposed approach.

关键词： Blind source separation Radial basis function networks Signal processing algorithms Independent component analysis Neurons Cost function backpropagation algorithms Kernel Information science Stochastic processes

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Enhancement of QRS complex using a neural network based ALE

Enhancement of QRS complex using a neural network based ALE

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Han-Go Choi Eun-Bo Shim School of Electronic Engineering Kum-Oh National University of Technology Gyeongbuk South Korea

Describes the application of a neural network based adaptive line enhancer (ALE) for enhancement of the QRS complex corrupted with background noise. Fully connected modified recurrent neural network, which is the combined structure of Elman's and Jordan's RNNs with one-to-many connections, is used as a nonlinear adaptive filter in the ALE. The connecting weights between network nodes as well as the parameters of the node activation function such as gain, slope, and delay are updated at each iteration using the error backpropagation algorithm. The proposed network is firstly evaluated by performing linear and nonlinear system identification. The real ECG signal buried with moderate and severe background noise is applied to the ALE using a nonlinear neural network adaptive filter in order to enhance the weak QRS complex. It is verified that the proposed network is suitable for use in system identification. Simulation results also show that the neural network based ALE performs well the enhancement of the QRS complex from noisy ECG signals.

关键词： Neural networks Background noise Recurrent neural networks Adaptive filters Electrocardiography Adaptive systems Line enhancers Joining processes Error correction backpropagation algorithms

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Radio network planning with neural networks

Radio network planning with neural networks

引用

IEEE Conference on Vehicular Technology (VTC)

作者： T. Binzer F.M. Landstorfer Institute of Radio Frequency Technology University of Stuttgart Stuttgart Germany

The increasing number of participants in modern mobile radio networks, especially with the prospect of the new CDMA systems, necessitates a more and more detailed and efficient radio network planning. The basis of network planning is always the prediction of the quality of transmission between the transmitter and the participant. In order to find the optimal location of the transmitters in a tolerable time, an automatic positioning of base stations in an urban area needs an accurate and fast propagation model. Presently, there is no satisfying combination of an accurate and fast propagation model and an algorithm which perform the positioning of base stations in large urban areas. A fast optimization algorithm for CDMA networks based on a fast and accurate propagation model is presented. For both, the coverage prediction and the optimization process neural networks are used. For the prediction of the coverage, which has to be optimized, a sophisticated backpropagation network is used whereas for the optimization process a self organizing map is applied. First results of the planning and optimization of an urban radio network based on these new algorithms are presented.

关键词： Radio network Neural networks Multiaccess communication Radio transmitters Base stations Urban areas backpropagation algorithms Land mobile radio Organizing Urban planning

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A new evolved artificial neural network and its application

A new evolved artificial neural network and its application

引用

World Congress on Intelligent Control and Automation (WCICA)

作者： Zhang Chunkai Li Yu Shao Huihe Institute of Automation Shanghai Jiaotong University Shanghai China Institute of LSI Shanghai Jiaotong University Shanghai China

Describes an evolved artificial neural network that is evolved by the particle swarm optimisation (PSO) algorithm. Compared with previous evolved ANNs, both the architecture and weights of this ANN are evolved by PSO, it means that the network architecture is adaptively adjusted, then the PSO algorithm is employed to evolve the nodes of ANNs with a given architecture. This process is repeated until the best network is accepted or the maximum number of generations has been reached. Some techniques, such as partial training algorithm (PT) and evolving added nodes (EAN), are used to maintain a closer behavioural link between the parents and their offspring, which will improve the efficiency of evolving ANNs. An ANN evolved is used in modelling a product quality estimator for a fractionator of the hydrocracking unit in the oil refining industry. The results show that the evolved ANN has good accuracy and generalisation ability.

关键词： Artificial neural networks Particle swarm optimization backpropagation algorithms Industrial training Evolutionary computation Genetic mutations Automation Large scale integration Fractionation Oil refineries

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Wrapped feature selection by means of guided neural network optimisation

Wrapped feature selection by means of guided neural network ...

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International Conference on Pattern Recognition

作者： B. Baesens S. Viaene J. Vanthienen G. Dedene Katholieke Universiteit Leuven Leuven Flanders BE

ISBN: (纸本)0769507506

We discuss the implementation of a wrapped neural network feature selection approach, introduced here as the weight cascaded retraining (WCR) algorithm. The paper provides an outline of the algorithm and elaborates on its formal underpinnings. Central to the whole feature pruning approach is the iteratively conceived guided function optimisation realised by passing the optimised weight vector from one iteration step to the next. This essentially gives rise to a cascaded form of neural network retraining. The theoretical exposition of the WCR algorithm is illuminated and benchmarked by means of the publicly available UCI case material. It is illustrated that WCR based neural network feature selection may be very effective in reducing model complexity for classification modelling via neural networks.

关键词： Neural networks Iterative algorithms Neurons Information systems Power generation economics Economic forecasting backpropagation algorithms Supervised learning Humans Skeleton

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A hyperbolic multilayer perceptron

A hyperbolic multilayer perceptron

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International Joint Conference on Neural Networks (IJCNN)

作者： S. Buchholz G. Sommer Department of Computer Science University of Kiel Kiel Germany

We present a novel MLP-type neural network based on hyperbolic numbers $the hyperbolic multilayer perceptron (HMLP). The neurons of the HMLP compute 2D-hyperbolic orthogonal transformations as weight propagation functions. The HMLP can therefore be seen as the hyperbolic counterpart of the known complex MLP. The HMLP is proven to be a universal approximator. Furthermore, a suitable backpropagation algorithm for it is derived. It is shown by experiments that the HMLP can learn tasks with underlying hyperbolic properties much more accurately and efficiently than a complex MLP and an ordinary MLP.

关键词： Multilayer perceptrons Neural networks Multi-layer neural network Neurons Algebra backpropagation algorithms Computer science Image reconstruction Computer architecture Robustness

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Non-standard norms in genetically trained neural networks

Non-standard norms in genetically trained neural networks

引用

IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (CoEN)

作者： A.K. Morales Centro de lnvestigación en Computación Unidad Profesional Adolfo López Mateos Instituto Politécnico Nacional Mexico

We discuss alternative norms to train neural networks (NNs). We focus on the so called multilayer perceptrons (MLPs). To achieve this we rely on a genetic algorithm called an eclectic GA (EGA). By using the EGA we avoid the drawbacks of the standard training algorithm in this sort of NN: the backpropagation algorithm. We define four measures of distance: the mean exponential error (MEE); the mean absolute error (MAE); the maximum square error (MSE); and the maximum (supremum) absolute error (SAE). We analyze the behavior of an MLP NN on two kinds of problems: classification and forecasting. We discuss the results of applying an EGA to train the NNs and show that alternative norms yield better results than the traditional RMS norm. We also discuss the resilience of genetically trained NNs to the change of the transfer function in the output layer.

关键词： Intelligent networks Neural networks Neurons backpropagation algorithms Transfer functions Convergence Multilayer perceptrons Genetic algorithms Resilience Economic forecasting

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Unsupervised learning of sigmoid perceptron

Unsupervised learning of sigmoid perceptron

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Z. Uykan H.N. Koivo Control Engineering Laboratory Helsinki University of Technology Finland

A previous paper has derived a clustering-based upper bound on mean squared output error of radial basis function networks that explicitly depends on the network parameters. In this study we focus on single-hidden-layer-sigmoid perceptron. Using the analysis of the previous paper, this paper (i) presents a similar upper bound on output error of the sigmoid perceptron and the upper bound can be made arbitrarily small by increasing the number of sigmoid units, and (ii) proposes unsupervised type learning of input-layer (synaptic) weights in contrast to traditional gradient-descent type supervised learning, i.e., the proposed method minimizes the upper bound by a clustering algorithm for determining the input-layer weights in contrast to the gradient-descent type algorithm minimizing the output error, which is traditionally used in the design of the perceptron. The simulation results show that (i) the proposed hierarchical method requires less time for learning when compared to gradient-descent-type supervised algorithm, (ii) it yields comparable performance in comparison with radial basis function network, and (iii) the upper bounds minimized during the clustering are quite tight to the output error function.

关键词： Unsupervised learning Clustering algorithms Neurons Vectors Algorithm design and analysis backpropagation algorithms Radial basis function networks Control engineering Laboratories Supervised learning

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PD control of robot with RBF networks compensation

PD control of robot with RBF networks compensation

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Wen Yu J.A. Heredia Departamento de Control Automatico CINVESTAV Mexicali Mexico

In this paper the popular PD controller of robot manipulator is modified. RBF neural networks are used to compensate the gravity and friction. No exact knowledge of the robot dynamics is required. The suggested learning law of neuro compensator is similar to the well-known backpropagation algorithm but with additional robust terms. Lyapunov-like analysis is used to derive the stability of learning algorithm.

关键词： PD control Radial basis function networks backpropagation algorithms Robot control Manipulator dynamics Neural networks Gravity Friction Robustness Algorithm design and analysis

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Fog forecasting using self growing neural network "CombNET-II"-a solution for imbalanced training sets problem

Fog forecasting using self growing neural network "CombNET-I...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： A.S. Nugroho S. Kuroyanagi A. Iwata Department of Electrical and Computer Engineering Nagoya Institute of Technology Nagoya Japan

Proposes a method to solve a problem that comes with imbalanced training sets which is often seen in practical applications. We modified the self growing neural network CombNET-II to deal with the imbalanced condition. This model is then applied to practical application which was launched in the '99 Fog Forecasting Contest sponsored by Neurocomputing Technical Group of IEICE, Japan. In this contest, a fog event should be predicted every 30 minutes based on the observation of meteorological conditions. CombNET-II achieved the highest accuracy among the participants and was chosen as the winner of the contest. The advantage of this model is that the independency of the branch networks contribute to an effective way of training and the time can be reduced.

关键词： Neural networks Weather forecasting Meteorology Artificial neural networks backpropagation algorithms Large-scale systems Neurons Databases Technology forecasting Load forecasting

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