Density Estimation Using a Generalized Neuron

Author

Kiran, Raveesh ; Venayagamoorthy, Ganesh K. ; Palaniswami, Marimuthu

Author_Institution

Dept. of Electr. & Comput. Eng., Missouri Univ., Rolla, MO

fYear

2006

fDate

10-13 July 2006

Firstpage

1

Lastpage

7

Abstract

Neural networks have been shown to be useful tools for density estimation. However, the training of neural network structures is time consuming and requires fast processors for practical applications. A new method with a generalized neuron (GN) for density estimation is presented in this paper. The GN is trained with the particle swarm optimization algorithm which is known to have fast convergence than the standard backpropagation algorithm. Results are presented to show that the GN can estimate the density functions for distribution functions with different means and variances. This density estimation method can also be applied to the multi-sensor data fusion process

Keywords

density measurement; neural nets; particle swarm optimisation; sensor fusion; density estimation; density function; distribution function; generalized neuron; multisensor data fusion process; neural network structure; particle swarm optimization algorithm; Backpropagation algorithms; Convergence; Density functional theory; Distribution functions; Function approximation; Neural network hardware; Neural networks; Neurons; Particle swarm optimization; Probability distribution; Density Estimation; generalized neuron; particle swarm optimization; probability distribution function;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2006 9th International Conference on

Conference_Location

Florence

Print_ISBN

1-4244-0953-5

Electronic_ISBN

0-9721844-6-5

Type

conf

DOI

10.1109/ICIF.2006.301715

Filename

4086001