Radial Basis Networks: Fundamentals and Applications for The Activation Functions of Artificial Neural Networks

What Is Radial Basis Networks

A radial basis function network is a type of artificial neural network that is used in the field of mathematical modeling. This type of network employs radial basis functions as its activation functions. The output of the network is a linear combination of the neuron parameters and the radial basis functions of the inputs to the network. Radial basis function networks have a wide range of applications, some of which include the approximation of functions, the prediction of time series, the classification of data, and the control of systems. In their study from 1988, Broomhead and Lowe, who were both researchers at the Royal Signals and Radar Establishment, were the ones who initially formulated the ideas.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Radial basis function network

Chapter 2: Gradient

Chapter 3: Radial basis function

Chapter 4: Radial basis function kernel

Chapter 5: Functional derivative

Chapter 6: Jacobian matrix and determinant

Chapter 7: Laplace's equation

Chapter 8: Laplace operator

Chapter 9: Multiple integral

Chapter 10: Polyharmonic spline

(II) Answering the public top questions about radial basis networks.

(III) Real world examples for the usage of radial basis networks in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of radial basis networks.

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