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
Recommended Citation
R. Kiran et al., "Density Estimation Using a Generalized Neuron," Proceedings of the 9th International Conference on Information Fusion, 2006, Institute of Electrical and Electronics Engineers (IEEE), Jan 2006.
The definitive version is available at https://doi.org/10.1109/ICIF.2006.301715
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.
Publication Date
01 Jan 2006
