Search Results - "Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)"

Estimating the mean and variance of the target probability distribution by Nix, D.A., Weigend, A.S.

“…Introduces a method that estimates the mean and the variance of the probability distribution of the target as a function of the input, given an assumed target…”
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Reinforcement learning in continuous time: advantage updating by Baird, L.C.

“…A new algorithm for reinforcement learning, advantage updating, is described. Advantage updating is a direct learning technique; it does not require a model to…”
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Neural networks in the Clifford domain by Pearson, J.K., Bisset, D.L.

“…Georgiou and Koutsougeras (1992) and Gordon et al. (1990) extended the traditional multi-layer perceptron to allow activation, threshold and weight values to…”
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Experience with adaptive probabilistic neural networks and adaptive general regression neural networks by Specht, D.F., Romsdahl, H.

“…By adapting separate smoothing parameters for each dimension, the classification accuracy of the the probabilistic neural network (PNN), and the estimation…”
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Feature selection and chromosome classification using a multilayer perceptron neural network by Lerner, B., Levinstein, M., Rosenberg, B., Guterman, H., Dinstein, L., Romem, Y.

“…Two feature selection techniques and a multilayer perceptron (MLP) neural network (NN) have been used in this study for human chromosome classification. The…”
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Predicting performance from test scores using backpropagation and counterpropagation by Fausett, L.V., Elwasif, W.

“…Two neural networks for general mapping problems, backpropagation and counterpropagation, are trained to predict students' grades in Calculus I from placement…”
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Modifying training algorithms for improved fault tolerance by Ching-Tai Chiu, Mehrotra, K., Mohan, C.K., Ranka, S.

“…This paper presents three approaches to improve fault tolerance of neural networks. In two approaches, the traditional backpropagation training algorithm is…”
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Wavelet neural networks are asymptotically optimal approximators for functions of one variable by Kreinovich, V., Sirisaengtaksin, O., Cabrera, S.

“…Neural networks are universal approximators. For example, it has been proved (K. Hornik et al., 1989) that for every /spl epsiv/>0 an arbitrary continuous…”
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Neural networks for financial market prediction by Chen, C.H.

“…The use of neural networks in financial market prediction presents a major challenge to the design of effective neural network predictors and classifiers. In…”
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Optimal radar pulse scheduling using a neural network by Izquierdo-Fuente, A., Casar-Corredera, J.R.

“…In a multifunction radar, the maximum number of targets which can be managed (maintain their tracks) is one of the main performance figures. The interleaving…”
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Image segmentation using pulse coupled neural networks by Ranganath, H.S., Kuntimad, G.

“…It is shown that if the parameters of a pulse coupled neural network are properly adjusted neurons corresponding to the pixels of each region can be forced to…”
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Development of feed-forward network models to predict gas consumption by Brown, R.H., Kharouf, P., Xin Feng, Piessens, L.P., Nestor, D.

“…The development of feedforward artificial neural network based models to predict gas consumption on a daily basis is the subject of this paper. An iterative…”
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Training controllers for robustness: multi-stream DEKF by Feldkamp, L.A., Puskorius, G.V.

“…Kalman-filter-based training has been shown to be advantageous in many training applications. By its nature, extended Kalman filter (EKF) training is realized…”
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Feature selection with distinction sensitive learning vector quantisation and genetic algorithms by Flotzinger, D., Pregenzer, M., Pfurtscheller, G.

“…Two feature selection methods, a distinction-sensitive learning vector quantizer (DSLVQ) and a genetic algorithm (GA) approach, are applied to multichannel…”
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Solving vehicle routing problems using elastic nets by Vakhutinsky, A.I., Golden, B.L.

“…Using neural networks to find an approximate solution to difficult optimization problems is a very attractive prospect. The traveling salesman problem (TSP),…”
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Optimal linear combinations of neural networks: an overview by Hashem, S., Schmeiser, B., Yih, Y.

“…Neural networks based modeling often involves trying multiple networks with different architectures and/or training parameters in order to achieve acceptable…”
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Wavelet neural networks employing over-complete number of compactly supported non-orthogonal wavelets and their applications by Yamakawa, T., Uchino, E., Samatsu, T.

“…This paper proposes two types of new neuron models, WS neuron (wavelet synapse neuron) and WA neuron (wavelet activation function neuron), which are obtained…”
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Next day peak load forecasting using an artificial neural network with modified backpropagation learning algorithm by Onoda, T.

“…This paper presents a method of next day peak load forecasting using an artificial neural network (ANN). The author combines the DSC search method (Davis,…”
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A study on the partitioning capabilities of two-layer neural networks by Che-Chern Lin, El-Jaroudi, A.

“…Recent studies indicate that neural networks with a single hidden layer can implement both nonconvex and disconnected decision regions. In this paper, we…”
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Intelligent control of flexible autonomous robots. I. Architectural considerations by Jagannathan, S., Evans, M.

“…A major deterrent to increased machining productivity is the lack of a unified approach to solve the planning, coordination, and decision making of intelligent…”
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