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

Meeting Name

9th International Conference on Information Fusion, 2006

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Density Estimation; Density Function; Density Measurement; Distribution Function; Generalized Neuron; Multisensor Data Fusion Process; Neural Nets; Neural Network Structure; Particle Swarm Optimization; Particle Swarm Optimization Algorithm; Probability Distribution Function; Sensor Fusion

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2006 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link

Share

 
COinS