Handbook of Neural Network Signal Processing

By Yu Hen Hu, Jeng-Neng Hwang, Jenq-Neng Hwang. IEEE Transactions on Neural Networks, 2000

378 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/03033c92c6efe62999373de0a55884517d6427e4

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In Artificial Intelligence

Citations in the corpus

Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network (2004, neutral): “Other new generation learning systems, such as support vector machines, are described in dedicated books [39,40].”
On learning optimized reaction diffusion processes for effective image restoration (2015, methodology): “We investigate two typical RBFs [23]: (1) Gaussian radial basis and (2) triangular-shaped radial basis.”
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration (2015, methodology): “r the quadratic loss function due to its simplicity. where ’represents different RBFs. In this paper, we exploit RBFs with equidistant centers jand uniﬁed scaling j. We investigate two typical RBFs [31]: (1) Gaussian radial basis ’ g and (2) triangular-shaped radial basis ’ t, given as ’ g(z) = ’ ”
Artificial intelligence for fault diagnosis of rotating machinery: A review (2018, methodology): “Multi-layer perceptron (MLP), is an ANN made of units arranged in layers with only forward connections to units in subsequent layers [80], which has been used in several applications.”
High-performance model reduction techniques in computational multiscale homogenization (2014, background): “The term “training”, which, incidentally, is borrowed from the neural network literature [38], is used throughout the text to refer to the offline generation of snapshots.”
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising (2017, background): “where πij are the expansion coefficients and μj are the centers of the basis functions ρj [17].”
Non-local Color Image Denoising with Convolutional Neural Networks (2016, methodology): “There are a few radial functions to choose from [17], but in this work we use Gaussian RBFs, ρj (r) = exp ( −εjr(2) ) .”
A survey of multiple classifier systems as hybrid systems (2014, neutral): “Regarding neural networks [129] it is easy to train pools of networks where each of them is trained starting from randomly chosen initial weights.”
Towards fully data driven ground-motion prediction models for Europe (2014, methodology): “The ANN type used in this work is the feed-forward ANN (Hu and Hwang 2002).”