Results 241 to 250 of about 274,124 (332)

AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing. [PDF]

Front Plant Sci
Narra M, Ray A, Polley B, Yang H, Bhowmik PK.   +4 more
europepmc   +1 more source

Effects of similarity networks in graph-based multi-omics classification. [PDF]

PLoS One
Siam MBH, Khan MR, Elahe MF, Arman MS, Akter S.   +4 more
europepmc   +1 more source

Machine learning-based prediction of well performance parameters for wellhead choke flow optimization. [PDF]

Sci Rep
Akbari A, Ghazi F, Kazemzadeh Y.
europepmc   +1 more source

Modeling tumor transport and growth with poroelastic biopolymer networks.

Soft Matter
Li Z, Ren Y, Kemkar S, Mollenkopf P, Kochanowski J, Janmey PA, Purohit PK, Radhakrishnan R, Vining KH.   +8 more
europepmc   +1 more source

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Radial Basis Function Networks

IEEE International Conference on Intelligent Systems, 2011
The design of a supervised neural network may be pursued in a variety of different ways. The back-propagation algorithm for the design of a multilayer perceptron (under supervision) as described in the previous chapter may be viewed as an application of ...
Age Eide, Thomas Lindblad, Guy Paillet
semanticscholar   +4 more sources

Universal Approximation Using Radial-Basis-Function Networks

Neural Computation, 1991
There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-
J, Park, I W, Sandberg
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Radial Basis Function Networks

2018
Radial basis function (RBF) networks represent a fundamentally different architecture from what we have seen in the previous chapters. All the previous chapters use a feed-forward network in which the inputs are transmitted forward from layer to layer in a similar fashion in order to create the final outputs.
C. Aggarwal
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Evolutionary Radial Basis Function Networks

Studies in Computational Intelligence, 2018
Radial Basis Function (RBF) networks are one of the most popular and applied type of neural networks. RBF networks are universal approximators and considered as special form of multilayer feedforward neural networks that contain only one hidden layer with Gaussian based activation functions.
Seyedali Mirjalili
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Generalization Performance of Radial Basis Function Networks

IEEE Transactions on Neural Networks and Learning Systems, 2015
This paper studies the generalization performance of radial basis function (RBF) networks using local Rademacher complexities. We propose a general result on controlling local Rademacher complexities with the L1 -metric capacity. We then apply this result to estimate the RBF networks' complexities, based on which a novel estimation error bound is ...
Yunwen, Lei, Lixin, Ding, Wensheng, Zhang   +2 more
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