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| Title: | Density estimation using a generalized neuron | |
| Author (s): | Kiran, R. Venayagamoorthy, Ganesh K. Palaniswami, M. | |
| Department/Lab Affiliations: | Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory | |
| Keywords: | 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 | |
| Issue Date: | 2006 | |
| Publisher: | Institute of Electrical and Electronics Engineers | |
| Citation: | Kiran, R.; Venayagamoorthy, G.K.; Palaniswami, M. "Density Estimation Using a Generalized Neuron" ICIF '06. 9th International Conference on Information Fusion, 2006. July 2006 Pages:1-7 | |
| 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 | |
| Type: | Article - Conference proceedings text | |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: | |
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| title | Density estimation using a generalized neuron | |
| contributor.author | Kiran, R. | |
| contributor.author | Venayagamoorthy, Ganesh K. | |
| contributor.author | Palaniswami, M. | |
| contributor.deptlab | Electrical and Computer Engineering | |
| contributor.deptlab | Real-Time Power and Intelligent Systems Laboratory | |
| subject | density estimation | |
| subject | density function | |
| subject | density measurement | |
| subject | distribution function | |
| subject | generalized neuron | |
| subject | multisensor data fusion process | |
| subject | neural nets | |
| subject | neural network structure | |
| subject | particle swarm optimization | |
| subject | particle swarm optimization algorithm | |
| subject | probability distribution function | |
| subject | sensor fusion | |
| date.issued | 2006 | |
| date.submitted | 2007 | |
| publisher | Institute of Electrical and Electronics Engineers | |
| identifier.citation | Kiran, R.; Venayagamoorthy, G.K.; Palaniswami, M. "Density Estimation Using a Generalized Neuron" ICIF '06. 9th International Conference on Information Fusion, 2006. July 2006 Pages:1-7 | |
| identifier.pub.URI | ||
| description.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 | |
| type | Article - Conference proceedings | |
| type.DCMIType | text | |
| type.status | Final version | |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | |
| rights.URI | ||
| date.accessioned | 2007-04-05T14:29:13Z | |
| date.available | 2007-04-05T14:29:13Z | |
| identifier.persist.URI | ||
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