Title :
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
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;
Conference_Titel :
Information Fusion, 2006 9th International Conference on
Conference_Location :
Florence
Print_ISBN :
1-4244-0953-5
Electronic_ISBN :
0-9721844-6-5
DOI :
10.1109/ICIF.2006.301715
