[ Pobierz całość w formacie PDF ]
.Given the goal cell and obstacle cells, the problem is to navigate the two-dimensional mobile robot from an unobstructed cell to the goal quickly, without colliding with any obstacle.An attracting artificial magnetic field is built for the goal location.They also build a repulsive artificial magnetic field around the boundary of each obstacle.Each neuron, a grid cell, will point to one of its eight neighbors, showing the direction for the movement of the robot.In other words, the Boltzmann machine is adapted to become a compass for the mobile robot.A ClassifierJames Ressler and Marijke Augusteijn study the use of neural networks to the problem of weapon to target assignment.The neural network is used as a filter to remove unfeasible assignments, where feasibility is determined in terms of the weapon’s ability to hit a given target if fired at a specific instant.The large number of weapons and threats along with the limitation on the amount of time lend significance to the need for reducing the number of assignments to consider.The network’s role here is classifier, as it needs to separate the infeasible assignments from the feasible ones.Learning has to be quick, and so Ressler and Augusteijn prefer to use an architecture called the cascade-correlation learning architecture, over backpropagation learning.Their network is dynamic in that the number of hidden layer neurons is determined during the training phase.This is part of a class of algorithms that change the architecture of the network during training.A Two-Stage Network for Radar Pattern ClassificationMohammad Ahmadian and Russell Pimmel find it convenient to use a multistage neural network configuration, a two-stage network in particular, for classifying patterns.The patterns they study are geometrical features of simulated radar targets.Feature extraction is done in the first stage, while classification is done in the second.Moreover, the first stage is made up of several networks, each for extracting a different estimable feature.Backpropagation is used for learning in the first stage.They use a single network in the second stage.The effect of noise is also studied.PreviousTable of ContentsNext | | Use of this site is subject to certain ,All rights reserved.Reproduction whole or in part in any form or medium without express written permision of EarthWeb is prohibited
[ Pobierz całość w formacie PDF ]