Radar signal categorization using a neural network

Article Abstract:

An energy-minimizing neural network analyzes a simulated radar environment filled with noisy pulses from many radar emitters. The 'brain state in a box' (BSB) network is a dynamic system that becomes stable after many iterations. BSB forms energy minima based on input data. The network cannot be trained to identify and count the emitters ahead of time because the correct answers are unknown. When pulses are received on the computational energy surface, the region they land on is added to an attractor. If the emitter does not spread its pulses too widely in state space, its pulses eventually all fall into the attractor. As the system learns more random noise, the system error will increase, so BSB networks can become 'senile.' A BSB is used to form a data base for emitter identification.

Neural networks, Algorithms, Radar, Algorithm, Radar Systems, Associative Memory, Adaptability, technical, Neural Network

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Sensitivity Analysis of the Ampacities of Self-Contained, Oil- Paper-Insulated, Naturally Cooled, and Forced Cooled 300-kV Underground Cables

Article Abstract:

A sensitivity analysis for a 300-kV cable studies the effects of various parameters on the ampacity. Oil-paper-insultated, naturally cooled, and forced cooled underground cable systems are considered. A table lists some results.

Cables, Power lines, Sensitivity Analysis, Transmission Lines

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