A Radial Basis Function Neural Network Approach to Two-Color Infrared Missile Detection
Read Online
Share

A Radial Basis Function Neural Network Approach to Two-Color Infrared Missile Detection

  • 94 Want to read
  • ·
  • 42 Currently reading

Published by Storming Media .
Written in English

Subjects:

  • TEC025000

Book details:

The Physical Object
FormatSpiral-bound
ID Numbers
Open LibraryOL11847301M
ISBN 101423528816
ISBN 109781423528814

Download A Radial Basis Function Neural Network Approach to Two-Color Infrared Missile Detection

PDF EPUB FB2 MOBI RTF

basis functions we need. Given there are four training patterns and two classes, M = 2 seems a reasonable first guess. We then need to decide on the basis function centres. The two separated zero targets seem a good bet, so we can set µ1 =(0,0) and µ2 =(1,1) and the distance between them is dmax = √2. We thus have the two basis functions φ1 1 2. OutlineIntroductionCommonly Used Radial Basis Functions Training RBFN RBF ApplicationsComparison Commonly Used Radial Basis Functions 1. Linear Function: ˚(r) = r 2. Cubic Function: ˚(r) = r3 3. Gaussian Function ˚(r) = exp(r2 2˙2) 4. Multi-Quadratic ˚(r) = (r2 + ˙2)1=2 5. Generalized Multi-Quadratic ˚(r) = (r2 + ˙2) ; 1 > >0 Size: KB. Radial basis function neural networks approaches, developed by Broomhead and Lowe in , are feed-forward networks which are trained by a supervised algorithm. They have been broadly used in classification and interpolation regression tasks,. Comparing to other neural networks, the RBFNNs are faster in their training phase and provide a better approximation due to their simpler network by: Because a three-layered neural network could approximate any arbitrary complicated non-linear relationship (Kolmogorov continuity theorem) (Chen and Billings, ), we have adopted, in this study, a three-layered radial basis function architecture of neural network to the inversion of seismic data in which one hidden layer with a variable number of neurons inside it Cited by:

In tro duction to Radial Basis F unction Net w orks Mark J L Orr Cen tre for Cognitiv e Science Univ ersit y of Edin burgh Buccleuc h Place Edin burgh EH L W Scotland April Abstract This do cumen tis anin tro duction to radial basis function RBF net w orks a t yp e of articial neural net w ork for application to Basis F unction Networks b File Size: 1MB. many basis functions are needed. Given there are four training patterns and two classes, M = 2 seems a reasonable first guess. Then the basis function centres need to be chosen. The two separated zero targets seem a good random choice, so µ 1 = (0,0) and µ 2 = (1,1) and the distance between them is d max = √2. That gives the basis functions. The Structure of the RBF Networks Radial Basis Functions are first introduced in the solution of the real multivariable interpolation problems. Broomhead and Lowe (), and Moody and Darken () were the first to exploit the use of radial basis functions in the design of neural networks. Radial Basis Function Neural Network Tutorial The Architecture of RBFNN’s The fig ure below shows a ra dial basis function neur al networ k. The be ll shaped cur ves in the hidden nodes indicate that eac h hidden lay er node repr esents a be ll shaped radial basis function that is centered on a vector in the feature Size: 63KB.

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. I, NO. 5, SEPTEMBER e Srinivasa V. Chakravarthy and Joydeep Ghosh Abstract- This paper shows how scale-based clustering can be done using the radial basis function (RBF) network (RBFN), with the RBF width as the scale paranleter and a dummy target as the desired output. In this paper, the applicability of the radial basis function (RBF) type artificial neural networks (ANNs) approach for modeling a hydrologic system is investigated. The method differs from the more widely used multilayer perceptron (MLP) approach in that the nonlinearity of the model is embedded only in the hidden layer of the network. Radial basis function (RBF) networks typically have three layers: an input layer, a hidden layer with a non-linear RBF activation function and a linear output layer. The input can be modeled as a vector of real numbers x ∈ R n {\displaystyle \mathbf {x} \in \mathbb {R} ^{n}}. Elanayar, S.V.T., Shin, Y.C.: Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems. IEEE Trans. Neural Network 5, – () CrossRef Google ScholarCited by: 8.