Neural network controller for a pulsed rocket motor tactical missile system
A neural network controller for a pulsed rocket motor tactical missile. The missile includes a fuselage or body, with a propulsion system. The pulsed propulsion system has a need for a logical control of the application of propulsion energy throughout the missile's flight. The controller is tra...
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creator | HOMER H. SCHWARTZ II JAMES E. BIGGERS KEVIN P. FINN |
description | A neural network controller for a pulsed rocket motor tactical missile. The missile includes a fuselage or body, with a propulsion system. The pulsed propulsion system has a need for a logical control of the application of propulsion energy throughout the missile's flight. The controller is trained to provide optimal initiation of individual rocket motor thrust pulses based on tactical information available at various points/times in the missile's flight. The controller training is through use of training cases, in which the network learns to output a specific target value(s) when specific values are input. When trained with a large sample of training cases selected from the multidimensional population of interest, the neural network effectively learns the correlations between inputs and outputs and can predict input/output relationships not previously seen in any training case. |
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The missile includes a fuselage or body, with a propulsion system. The pulsed propulsion system has a need for a logical control of the application of propulsion energy throughout the missile's flight. The controller is trained to provide optimal initiation of individual rocket motor thrust pulses based on tactical information available at various points/times in the missile's flight. The controller training is through use of training cases, in which the network learns to output a specific target value(s) when specific values are input. When trained with a large sample of training cases selected from the multidimensional population of interest, the neural network effectively learns the correlations between inputs and outputs and can predict input/output relationships not previously seen in any training case.</description><edition>6</edition><language>eng</language><subject>AMMUNITION ; BLASTING ; EXPLOSIVE CHARGES, e.g. FOR BLASTING, FIREWORKS, AMMUNITION ; HEATING ; LIGHTING ; MECHANICAL ENGINEERING ; WEAPONS</subject><creationdate>2000</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20000105&DB=EPODOC&CC=AU&NR=6237199A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20000105&DB=EPODOC&CC=AU&NR=6237199A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HOMER H. 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When trained with a large sample of training cases selected from the multidimensional population of interest, the neural network effectively learns the correlations between inputs and outputs and can predict input/output relationships not previously seen in any training case.</description><subject>AMMUNITION</subject><subject>BLASTING</subject><subject>EXPLOSIVE CHARGES, e.g. FOR BLASTING, FIREWORKS, AMMUNITION</subject><subject>HEATING</subject><subject>LIGHTING</subject><subject>MECHANICAL ENGINEERING</subject><subject>WEAPONS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2000</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZPD2Sy0tSsxRyEstKc8vylZIzs8rKcrPyUktUkjLL1JIVCgozSlOTVEoyk_OTi1RyM0vAYqWJCaXZCYDdeVmFhdn5qQqFFcWl6Tm8jCwpiUCVfNCaW4GeTfXEGcP3dSC_PjU4oLE5FSgNfGOoWZGxuaGlpaOxoRVAADPQTVF</recordid><startdate>20000105</startdate><enddate>20000105</enddate><creator>HOMER H. SCHWARTZ II</creator><creator>JAMES E. BIGGERS</creator><creator>KEVIN P. FINN</creator><scope>EVB</scope></search><sort><creationdate>20000105</creationdate><title>Neural network controller for a pulsed rocket motor tactical missile system</title><author>HOMER H. SCHWARTZ II ; JAMES E. BIGGERS ; KEVIN P. FINN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_AU6237199A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2000</creationdate><topic>AMMUNITION</topic><topic>BLASTING</topic><topic>EXPLOSIVE CHARGES, e.g. FOR BLASTING, FIREWORKS, AMMUNITION</topic><topic>HEATING</topic><topic>LIGHTING</topic><topic>MECHANICAL ENGINEERING</topic><topic>WEAPONS</topic><toplevel>online_resources</toplevel><creatorcontrib>HOMER H. SCHWARTZ II</creatorcontrib><creatorcontrib>JAMES E. BIGGERS</creatorcontrib><creatorcontrib>KEVIN P. FINN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HOMER H. SCHWARTZ II</au><au>JAMES E. BIGGERS</au><au>KEVIN P. FINN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Neural network controller for a pulsed rocket motor tactical missile system</title><date>2000-01-05</date><risdate>2000</risdate><abstract>A neural network controller for a pulsed rocket motor tactical missile. The missile includes a fuselage or body, with a propulsion system. The pulsed propulsion system has a need for a logical control of the application of propulsion energy throughout the missile's flight. The controller is trained to provide optimal initiation of individual rocket motor thrust pulses based on tactical information available at various points/times in the missile's flight. The controller training is through use of training cases, in which the network learns to output a specific target value(s) when specific values are input. When trained with a large sample of training cases selected from the multidimensional population of interest, the neural network effectively learns the correlations between inputs and outputs and can predict input/output relationships not previously seen in any training case.</abstract><edition>6</edition><oa>free_for_read</oa></addata></record> |
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subjects | AMMUNITION BLASTING EXPLOSIVE CHARGES, e.g. FOR BLASTING, FIREWORKS, AMMUNITION HEATING LIGHTING MECHANICAL ENGINEERING WEAPONS |
title | Neural network controller for a pulsed rocket motor tactical missile system |
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