Einsteinian neural network for spectrum estimation

✍ Scribed by Leonid I. Perlovsky; Charles P. Plum; Peter R. Franchi; Elihu J. Tichovolsky; David Choi; Bertus Weijers

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 555 KB
• Volume: 10
• Category: Article
• ISSN: 0893-6080
• DOI: 10.1016/s0893-6080(97)00081-6

✦ Synopsis

A model-based neural network is developed for spectrum estimation. Its architecture and learning mechanism are founded on the Einsteinian interpretation of the spectrum as a probability distribution of photons. By considering a spectrum as an ensemble of photons, we derive the neural learning mechanism from the basic physical principle of entropy maximization of a canonical ensemble. This neural network is applied to characterizing a recently observed phenomenon known as equatorial ionospheric clutter that significantly affects operations of over-the-horizon (OTH) radars and communication links using high frequency radiowaves propagating through the ionosphere. We utilize a specific parameterization of the internal spectral model, which is derived from the physical principles of the propagation of electromagnetic waves through a turbulent ionosphere. A set of parameters characterizing equatorial ionospheric clutter is estimated. The developed technique may have a broad applicability in scientific data analysis.