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Investigation of Cutting Forces and Temperature in Face Milling of Wood-Plastic Composite Using Radial Basis Function Neural Network. [PDF]

Materials (Basel)
Ji F, Zhu Z.
europepmc   +1 more source

Behavior of Electrothermal Actuator Analyzed by Polynomial Point Interpolation Collocation Method. [PDF]

Micromachines (Basel)
Tang Y, Qi A, Gu Y, Zhu Y, Li H, Gu D, Chen H.   +6 more
europepmc   +1 more source

A Rapid Prediction Method for Underwater Vehicle Radiated Noise Based on Feature Selection and Parallel Residual Neural Network. [PDF]

Sensors (Basel)
Ji F, Li Z, Feng W, Shi M, Ji X.
europepmc   +1 more source

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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-
Jooyoung Park, I. Sandberg
semanticscholar   +3 more sources

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
semanticscholar   +2 more sources

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
semanticscholar   +2 more sources

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, L. Ding, Wensheng Zhang
semanticscholar   +3 more sources

Radial Basis Function Networks

Encyclopedia of Machine Learning, 2002
M. Buhmann
semanticscholar   +5 more sources