Feedforward Neural Network Models for FPGA Routing Channel Width Estimation

Liu, Qiang; Gao, Ming; Zhang, Tao; Zhang, Qi‐Jun

doi:10.1049/cje.2016.01.011

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…As a more universal new neural network model, Extreme learning machine (ELM) [ 1 ] , [ 2 ] has been widely applied to computer vision [ 3 ] , [ 4 ] , FPGA architecture development [ 5 ] , biomedical [ 6 ] and many other fields. Different from the traditional artificial neural network with complex network structure, the extreme learning machine is a single hidden layer model, that is, the entire network structure is composed of an input layer, a hidden layer and an output layer.…”

Section: Introductionmentioning

confidence: 99%

Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM

Wang

Jijuan

Cheng

et al. 2021

Chinese J of Electronics

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In the problem of unsupervised domain adaption Extreme learning machine (ELM), the output layer parameters need to have both classification and domain adaptation functions, which often cannot be simultaneously fully utilized. In addition, traditional matching method based on data probability distribution cannot find the common subspace of source and target domains under large difference between domains. In order to alleviate the pressure of double functions of classifier parameters, the entire ELM learning process is mainly divided into two stages: feature representation and adaptive classifier learning, thus a joint feature representation and classifier learning based unsupervised domain adaption ELM model is proposed. In the feature representation stage, the source and target domain data are projected to their respective subspace while minimizing the difference in probability distribution between the two domains. In the adaptive classifier learning stage, the smooth manifold regularization term of target domain is used to improve the parameter adaptive ability. Experiments on six different types of datasets show that the proposed model has higher cross‐domain classification accuracy.

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