نتایج جستجو برای: weight updating

تعداد نتایج: 369162  

Journal: :journal of artificial intelligence in electrical engineering 0

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

Journal: :Applied Artificial Intelligence 2020

Journal: :British Journal of Sports Medicine 2009

2011
Yuke FANG Yan FU Chongjing SUN Junlin ZHOU

From family of corrective boosting algorithms (i.e. AdaBoost, LogitBoost) to total corrective algorithms (i.e. LPBoost, TotalBoost, SoftBoost, ERLPBoost), we analysis these methods of sample weight updating. Corrective boosting algorithms update the sample weight according to the last hypothesis; comparatively, total corrective algorithms update the weight with the best one of all weak classifi...

Journal: :Neurocomputing 2011
Sang-Hoon Oh

Classification of imbalanced data is pervasive but it is a difficult problem to solve. In order to improve the classification of imbalanced data, this letter proposes a new error function for the error backpropagation algorithm of multilayer perceptrons. The error function intensifies weight-updating for the minority class and weakens weight-updating for the majority class. We verify the effect...

Journal: :The Journal of Korea Institute of Information, Electronics, and Communication Technology 2016

2006
D. S. Osipov

In this paper a Code Division Multiple Access detector based on a neural network, which is proposed in [4], is considered. It is shown how the choice of an updating scheme and/or the network parameters influences the detector performance. A weight updating scheme that might be used instead of the classical ones is proposed.

Journal: :CoRR 2017
Suhwan Lim Jong-Ho Bae Jai-Ho Eum Sungtae Lee Chul-Heung Kim Byung-Gook Park Jong-Ho Lee

In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of po...

2010
Shiow-Jyu Lin Yi-Tsan Hung Wen-Jyi Hwang

This paper presents a novel hardware architecture based on generalized Hebbian algorithm (GHA) for texture classification. In the architecture, the weight vector updating process is separated into a number of stages for lowering area costs and increasing computational speed. Both the weight vector updating process and principle component computation process can also operate concurrently to furt...

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