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Wavelet networks as an alternative to neural networks

Wavelet networks as an alternative to neural networks

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： I. Ciuca J.A. Ware Research Institute for Informatics Bucharest Romania Glamorgan University Pontypridd UK

The paper presents an alternative to the use of feedfoward neural networks as universal approximators. The alternative is based on the wavelet approximation theory of nonlinear functions. An algorithm from the evolutionary computation class is presented for wavelet network learning which as an optional facility, incorporates the capability for removing irrelevant features from input data in classification applications. The results of a dynamic process forecasting application are also presented.

关键词： Neural networks Feedforward neural networks Feedforward systems backpropagation algorithms Principal component analysis Evolutionary computation Genetic programming Informatics Approximation methods Genetic algorithms

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An adaptive and fully sparse training approach for multilayer perceptrons

An adaptive and fully sparse training approach for multilaye...

引用

International Conference on Neural Networks

作者： Fang Wang Q.J. Zhang Department of Electronics Carleton University Ottawa Canada

An adaptive and fully sparse backpropagation training approach is proposed in this paper. The technique speeds up training by combining a sparse optimization concept with neural network training. The sparse phenomenon due to neuron activation, which is inherent in neural networks, is exploited in both feedforward and backpropagation phases. A new computational algorithm with sparse pattern reuse and refreshment has been developed together with the adaptation procedure of a new set of parameters which regulate the sparse training process. The proposed training approach has been applied to speech recognition and circuit extraction problems and achieved significant speed-up of training.

关键词： Multilayer perceptrons Neurons Neural networks Jacobian matrices backpropagation algorithms Speech recognition Circuits Optimization methods Feedforward neural networks Gradient methods

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A learning machine for resource-limited adaptive hardware

A learning machine for resource-limited adaptive hardware

引用

NASA/ESA Conference on Adaptive Hardware and Systems (AHS)

作者： Davide Anguita Alessandro Ghio Stefano Pischiutta DIBE-Department of Biophysical and Electronic Engineering University of Genoa Genoa Italy

Machine learning algorithms allow to create highly adaptable systems, since their functionality only depends on the features of the inputs and the coefficients found during the training stage. In this paper, we present a method for building support vector machines (SVM), characterized by integer parameters and coefficients. This method is useful to implement a pattern recognition system on resource-limited hardware, where a floating-point unit is often unavailable.

关键词： Machine learning Hardware Support vector machines Pattern recognition Signal processing algorithms Feedforward systems Microprocessors Support vector machine classification Machine learning algorithms backpropagation algorithms

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Minimizing Distribution Cost of Distributed Neural Networks in Wireless Sensor Networks

Minimizing Distribution Cost of Distributed Neural Networks ...

引用

IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference

作者： Peng Guan Xiaolin Li Scalable Software Systems Laboratory Department of Computer Science Oklahoma State University Stillwater OK USA

This paper presents a novel study on how to distribute neural networks in a wireless sensor networks (WSNs) such that the energy consumption is minimized while improving the accuracy and training efficiency. Artificial neural network (ANN) learning has been shown robust to noisy and uncertain sensory data for function approximation and pattern classification applications. With the advances of miniature hardware technologies for powerful sensor nodes, embedded neural networks will emerge as important decision-making brains for WSNs and vast surveillance applications to enable adaptive data quality and self-managing capabilities. To distribute neural networks in WSNs in an energy-efficient manner, we propose parallel transmission and adaptive neural selection algorithms(ANSA) in multilayer backpropagation(MLBP) learning process of neural networks, which is a popular supervised learning technique used for training feedforward artificial neural networks. We further analyze the energy consumption components in the online training process and evaluate the reduced energy consumption using our proposed algorithms.

关键词： Costs Neural networks Wireless sensor networks Artificial neural networks Biological neural networks Energy consumption Multi-layer neural network Feedforward neural networks backpropagation algorithms Robustness

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A parameter estimation approach to artificial neural network weight selection for nonlinear system identification

A parameter estimation approach to artificial neural network...

引用

IEEE Conference on Control Technology and Applications (CCTA)

作者： T.L. Ruchti R.H. Brown J.J. Garside Department of Electrical and Computer Engineering Marquette University Milwaukee WI USA

A unified framework for artificial neural network (ANN) training algorithms applied to nonlinear system identification based on considering weight selection as a parameter estimation problem is presented. Three existing ANN training strategies are reviewed within this framework, including gradient-descent backpropagation, the extended Kalman algorithm, and the recursive-least-squares method. A strikingly different approach to error backpropagation is presented, resulting in the development of a novel method of backward signal propagation and target state generation for embedded layers. The technique is suitable for implementation with a linear Kalman-based update algorithm and is applied with a unique method of covariance modification for the elimination of transients associated with initial conditions. Experimental nonlinear identification results demonstrate a greatly increased rate of convergence in comparison with backpropagation. The new algorithm displayed similar rates of parameter convergence and a decreased computational overhead compared with the extended Kalman algorithm.< >

关键词： Parameter estimation Artificial neural networks Nonlinear systems backpropagation algorithms Neurons Kalman filters Convergence System identification Control systems Neural networks

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Optimizing Neural Net based Predictive Control

Optimizing Neural Net based Predictive Control

引用

American Control Conference (ACC)

作者： Jean Saint Donat Naveen Bhat Thomas J. McAvoy Department of Chemical Engineering University of Maryland College Park MD USA

Neural networks hold great promise for application in the general area of process control. This paper focuses on using a backpropagation network in an optimization based model predictive control scheme. Since analytical expressions for the gradient and Hessian of the neural net model can be derived and these expressions can be calculated in paralle, extremely fast computation times are possible. The control approach is illustrated on a pH CSTR example.

关键词： Neural networks Predictive control Biological neural networks Neurons backpropagation algorithms Continuous-stirred tank reactor Computer architecture Speech analysis Algorithm design and analysis Chemicals

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Comparison of TDNN training algorithms in brain machine interfaces

Comparison of TDNN training algorithms in brain machine inte...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yiwen Wang Sung-Phil Kim J.C. Principe Computational NeuroEngineering Laboratory University of Florida Gainesville FL USA

Linear or non-linear models are used in brain machine interfaces (BIMIs) to map the neural activity to the associated behavior, typically the primate's hand position. Linear models assume a linear relationship between neural activity and hand position that may not be the case. A solution would be time-delay neural network (TDNN) that provides effectively a nonlinear combination of linear models. However, this model results in a drastic increase of free parameters and slow convergence when trained by an error backpropagation learning rule. We propose to train the TDNN by scaled conjugate gradient, which avoids time-consuming linear search, coupled with weight decay to reduce the free parameters number and produce generally faster convergence.

关键词： Convergence Brain modeling Error correction Biological neural networks Cost function Finite impulse response filter backpropagation algorithms Brain computer interfaces Computer interfaces Neural engineering

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Neural networks for sensor fusion in remote sensing

Neural networks for sensor fusion in remote sensing

引用

International Joint Conference on Neural Networks (IJCNN)

作者： H. Pasika S. Haykin E. Clothiaux R. Stewart McMaster University Hamilton ONT Canada Pennslyvania State University PA USA Environment Canada Downsview ONT Canada

Cloud base height is a continuous variable that falls within the range of zero to fourteen kilometers and is useful for understanding the Earth's radiation budget. Advances in LIDAR (laser radar) technology have provided accurate cloud base height measurements. However, new sensor development and deployment are costly processes. The paper is motivated by a desire to make LIDAR output of cloud base height information available at a network of ground based meteorological stations without actually installing LIDAR sensors. To accomplish this, fifty-seven sensors ranging from multispectral satellite information to standard atmospheric measurements such as temperature and humidity, are fused in what can only be termed as a very complex, non-linear environment. The result is an accurate prediction of cloud base height. Thus, a virtual sensor is created. This fusion is performed via neural network architectures. More specifically the choices of learning algorithms reflect the state-of-the-art in neural network design and include; as local methods, the regularized radial basis function (RBF) network and the support vector machine (SVM). Global methods include the node decoupled extended Kalman filter trained multi-layer perception (NDEKF-MLP), and as a benchmark, the venerable backpropagation algorithm. Overall, the support vector machine has shown itself to be the method of choice especially when complexity was considered, Excessive storage requirements occurred in the RBF case and the global methods required large committee machines to overcome the effects of local minima.

关键词： Neural networks Sensor fusion Remote sensing Laser radar Clouds Support vector machines Temperature sensors backpropagation algorithms Earth Laser fusion

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A two-step approach for improving efficiency of feedforward Multilayer Perceptrons network

A two-step approach for improving efficiency of feedforward ...

引用

International Conference on Information and Communication Technologies (ICICT)

作者： Shoukat Ullah Zakia Hussain Higher Education Department NWFP Pakistan NWFP Pakistan

An artificial neural network has got greater importance in the field of data mining. Although it may have complex structure, long training time, and uneasily understandable representation of results, neural network has high accuracy and is preferable in data mining. This research paper is aimed to improve efficiency and to provide accurate results on the basis of same behaviour data. To achieve these objectives, an algorithm is proposed that uses two data mining techniques, that is, attribute selection method and cluster analysis. The algorithm works by applying attribute selection method to eliminate irrelevant attributes, so that input dimensionality is reduced to only those attributes which contribute in the training process. Then after, the whole dataset is partitioned into n clusters which are finally fed into multilayer perceptrons network based on backpropagation algorithm to carry out blockwise and parallel training.

关键词： Multilayer perceptrons Artificial neural networks Data mining backpropagation algorithms Neural networks Clustering algorithms Data analysis Psychology System testing Computer networks

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Parallel tangent methods with variable stepsize

Parallel tangent methods with variable stepsize

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Y.G. Petalas M.N. Vrahatis Department of Mathematics University of Patras Artificial Intelligence Research Center (UPAIRC) Patras Greece

The most widely used algorithm for training multilayer feedforward neural networks is backpropagation. Back-propagation is an iterative gradient descent algorithm. Since its appearance, various methods which modify the conventional BP have been created to improve its efficiency. One such algorithm which uses an adaptive learning rate is backpropagation with variable stepsize, is proposed. Parallel tangent methods are used in global optimization to modify and improve the simple gradient descent algorithm by using from time to time the difference between the current point and the point before two steps as a search direction, instead of using the gradient. In this study, we investigate the combination of the BPVS method with the parallel tangent approach for neural network training. We perform experimental results on well-known test problems to evaluate the efficiency of the method.

关键词： backpropagation algorithms Iterative algorithms Neural networks Feedforward neural networks Error correction Equations Mathematics Artificial intelligence Electronic mail Artificial neural networks

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