A Novel Neural-Fuzzy Guidance Law Design by Applying Different Neural Network Optimization Algorithms Alternatively for Each Step View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Jium-Ming Lin , Cheng-Hung Lin

ABSTRACT

In this research, a novel neural-fuzzy guidance law by applying different neural network optimization algorithms alternatively in each step is proposed, such as the Gradient Descent (GD), SCG (Scaled Conjugate Gradient), and Levenberg-Marquardt (LM) methods are applied to deal with those parameter variation effects as follows: target maneuverability, missile autopilot time constant, turning rate time constant and radome slope error effects. Comparing with the proportion navigation (PN) and fuzzy methods are also made; the miss distances obtained by the proposed method are lower, and the proposed acceleration commands are always without polarity changes or oscillation at the final stage. More... »

PAGES

292-301

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-05458-2_31

DOI

http://dx.doi.org/10.1007/978-3-319-05458-2_31

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011788038


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Numerical and Computational Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Communication Engineering, Chung-Hua University, 707, Sec 2, Wu-Fu Rd, 30012, Hsin-Chu, Taiwan, ROC", 
          "id": "http://www.grid.ac/institutes/grid.411655.2", 
          "name": [
            "Department of Communication Engineering, Chung-Hua University, 707, Sec 2, Wu-Fu Rd, 30012, Hsin-Chu, Taiwan, ROC"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lin", 
        "givenName": "Jium-Ming", 
        "id": "sg:person.014307645440.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014307645440.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ph. D. Program in Engineering Science, College of Engng., Chung-Hua University, 707, Sec 2, Wu-Fu Rd, 30012, Hsin-Chu, Taiwan, ROC", 
          "id": "http://www.grid.ac/institutes/grid.411655.2", 
          "name": [
            "Ph. D. Program in Engineering Science, College of Engng., Chung-Hua University, 707, Sec 2, Wu-Fu Rd, 30012, Hsin-Chu, Taiwan, ROC"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lin", 
        "givenName": "Cheng-Hung", 
        "id": "sg:person.0621614254.04", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621614254.04"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2014", 
    "datePublishedReg": "2014-01-01", 
    "description": "In this research, a novel neural-fuzzy guidance law by applying different neural network optimization algorithms alternatively in each step is proposed, such as the Gradient Descent (GD), SCG (Scaled Conjugate Gradient), and Levenberg-Marquardt (LM) methods are applied to deal with those parameter variation effects as follows: target maneuverability, missile autopilot time constant, turning rate time constant and radome slope error effects. Comparing with the proportion navigation (PN) and fuzzy methods are also made; the miss distances obtained by the proposed method are lower, and the proposed acceleration commands are always without polarity changes or oscillation at the final stage.", 
    "editor": [
      {
        "familyName": "Nguyen", 
        "givenName": "Ngoc Thanh", 
        "type": "Person"
      }, 
      {
        "familyName": "Attachoo", 
        "givenName": "Boonwat", 
        "type": "Person"
      }, 
      {
        "familyName": "Trawi\u0144ski", 
        "givenName": "Bogdan", 
        "type": "Person"
      }, 
      {
        "familyName": "Somboonviwat", 
        "givenName": "Kulwadee", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-05458-2_31", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-05457-5", 
        "978-3-319-05458-2"
      ], 
      "name": "Intelligent Information and Database Systems", 
      "type": "Book"
    }, 
    "keywords": [
      "gradient descent", 
      "neural network optimization", 
      "neural network optimization algorithm", 
      "network optimization algorithm", 
      "network optimization", 
      "optimization algorithm", 
      "fuzzy method", 
      "Levenberg-Marquardt method", 
      "target maneuverability", 
      "acceleration command", 
      "error effects", 
      "guidance law", 
      "guidance law design", 
      "navigation", 
      "miss distance", 
      "algorithm", 
      "parameter variation effects", 
      "command", 
      "law design", 
      "method", 
      "optimization", 
      "step", 
      "maneuverability", 
      "design", 
      "time", 
      "variation effects", 
      "research", 
      "descent", 
      "distance", 
      "final stage", 
      "SCG", 
      "stage", 
      "rate time", 
      "law", 
      "changes", 
      "polarity change", 
      "effect", 
      "oscillations", 
      "novel neural-fuzzy guidance law", 
      "neural-fuzzy guidance law", 
      "different neural network optimization", 
      "missile autopilot time", 
      "autopilot time", 
      "radome slope error effects", 
      "slope error effects", 
      "proportion navigation", 
      "Novel Neural-Fuzzy Guidance Law Design", 
      "Neural-Fuzzy Guidance Law Design", 
      "Different Neural Network Optimization Algorithms"
    ], 
    "name": "A Novel Neural-Fuzzy Guidance Law Design by Applying Different Neural Network Optimization Algorithms Alternatively for Each Step", 
    "pagination": "292-301", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1011788038"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-05458-2_31"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-05458-2_31", 
      "https://app.dimensions.ai/details/publication/pub.1011788038"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:27", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_69.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-05458-2_31"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-05458-2_31'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-05458-2_31'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-05458-2_31'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-05458-2_31'


 

This table displays all metadata directly associated to this object as RDF triples.

141 TRIPLES      23 PREDICATES      77 URIs      68 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-05458-2_31 schema:about anzsrc-for:01
2 anzsrc-for:0103
3 anzsrc-for:08
4 anzsrc-for:0801
5 schema:author N7d166b0e6a5e4f58b07100670819e8e6
6 schema:datePublished 2014
7 schema:datePublishedReg 2014-01-01
8 schema:description In this research, a novel neural-fuzzy guidance law by applying different neural network optimization algorithms alternatively in each step is proposed, such as the Gradient Descent (GD), SCG (Scaled Conjugate Gradient), and Levenberg-Marquardt (LM) methods are applied to deal with those parameter variation effects as follows: target maneuverability, missile autopilot time constant, turning rate time constant and radome slope error effects. Comparing with the proportion navigation (PN) and fuzzy methods are also made; the miss distances obtained by the proposed method are lower, and the proposed acceleration commands are always without polarity changes or oscillation at the final stage.
9 schema:editor Ndbe7b9c0a700406eaf31b1a695aab25b
10 schema:genre chapter
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf N5aff9b25117c407085b4f23adb3d45a4
14 schema:keywords Different Neural Network Optimization Algorithms
15 Levenberg-Marquardt method
16 Neural-Fuzzy Guidance Law Design
17 Novel Neural-Fuzzy Guidance Law Design
18 SCG
19 acceleration command
20 algorithm
21 autopilot time
22 changes
23 command
24 descent
25 design
26 different neural network optimization
27 distance
28 effect
29 error effects
30 final stage
31 fuzzy method
32 gradient descent
33 guidance law
34 guidance law design
35 law
36 law design
37 maneuverability
38 method
39 miss distance
40 missile autopilot time
41 navigation
42 network optimization
43 network optimization algorithm
44 neural network optimization
45 neural network optimization algorithm
46 neural-fuzzy guidance law
47 novel neural-fuzzy guidance law
48 optimization
49 optimization algorithm
50 oscillations
51 parameter variation effects
52 polarity change
53 proportion navigation
54 radome slope error effects
55 rate time
56 research
57 slope error effects
58 stage
59 step
60 target maneuverability
61 time
62 variation effects
63 schema:name A Novel Neural-Fuzzy Guidance Law Design by Applying Different Neural Network Optimization Algorithms Alternatively for Each Step
64 schema:pagination 292-301
65 schema:productId N358284bc307c4b859d51ce5d2100b83c
66 N4fa50adddb5d433c8f97e76bdc1d0318
67 schema:publisher N2e9679e37870421a9da676c2a5969af0
68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011788038
69 https://doi.org/10.1007/978-3-319-05458-2_31
70 schema:sdDatePublished 2022-01-01T19:27
71 schema:sdLicense https://scigraph.springernature.com/explorer/license/
72 schema:sdPublisher N641a327d41ac49e9932da574595b8381
73 schema:url https://doi.org/10.1007/978-3-319-05458-2_31
74 sgo:license sg:explorer/license/
75 sgo:sdDataset chapters
76 rdf:type schema:Chapter
77 N20d73ea074f1453297a2b83711eda675 rdf:first N79cb834381bc42a4a0bc0ddebfd0fbdc
78 rdf:rest Nb9eb1c983bf24a91ae4598cafa134773
79 N2e9679e37870421a9da676c2a5969af0 schema:name Springer Nature
80 rdf:type schema:Organisation
81 N358284bc307c4b859d51ce5d2100b83c schema:name dimensions_id
82 schema:value pub.1011788038
83 rdf:type schema:PropertyValue
84 N42984ca859e545c08a833bf2808c4bde schema:familyName Trawiński
85 schema:givenName Bogdan
86 rdf:type schema:Person
87 N4fa50adddb5d433c8f97e76bdc1d0318 schema:name doi
88 schema:value 10.1007/978-3-319-05458-2_31
89 rdf:type schema:PropertyValue
90 N5aff9b25117c407085b4f23adb3d45a4 schema:isbn 978-3-319-05457-5
91 978-3-319-05458-2
92 schema:name Intelligent Information and Database Systems
93 rdf:type schema:Book
94 N601b58a9aef64a3890dcc68be0c4868a rdf:first N8b11206985b34d86896eace9def2ac83
95 rdf:rest rdf:nil
96 N641a327d41ac49e9932da574595b8381 schema:name Springer Nature - SN SciGraph project
97 rdf:type schema:Organization
98 N79cb834381bc42a4a0bc0ddebfd0fbdc schema:familyName Attachoo
99 schema:givenName Boonwat
100 rdf:type schema:Person
101 N7d166b0e6a5e4f58b07100670819e8e6 rdf:first sg:person.014307645440.05
102 rdf:rest Nb66ee063e8fe47d9ae7401f593baca88
103 N8b11206985b34d86896eace9def2ac83 schema:familyName Somboonviwat
104 schema:givenName Kulwadee
105 rdf:type schema:Person
106 Nb66ee063e8fe47d9ae7401f593baca88 rdf:first sg:person.0621614254.04
107 rdf:rest rdf:nil
108 Nb9eb1c983bf24a91ae4598cafa134773 rdf:first N42984ca859e545c08a833bf2808c4bde
109 rdf:rest N601b58a9aef64a3890dcc68be0c4868a
110 Ndbe7b9c0a700406eaf31b1a695aab25b rdf:first Nef0cc2b98e1944699a9d8b629ea9acc9
111 rdf:rest N20d73ea074f1453297a2b83711eda675
112 Nef0cc2b98e1944699a9d8b629ea9acc9 schema:familyName Nguyen
113 schema:givenName Ngoc Thanh
114 rdf:type schema:Person
115 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
116 schema:name Mathematical Sciences
117 rdf:type schema:DefinedTerm
118 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
119 schema:name Numerical and Computational Mathematics
120 rdf:type schema:DefinedTerm
121 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
122 schema:name Information and Computing Sciences
123 rdf:type schema:DefinedTerm
124 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
125 schema:name Artificial Intelligence and Image Processing
126 rdf:type schema:DefinedTerm
127 sg:person.014307645440.05 schema:affiliation grid-institutes:grid.411655.2
128 schema:familyName Lin
129 schema:givenName Jium-Ming
130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014307645440.05
131 rdf:type schema:Person
132 sg:person.0621614254.04 schema:affiliation grid-institutes:grid.411655.2
133 schema:familyName Lin
134 schema:givenName Cheng-Hung
135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0621614254.04
136 rdf:type schema:Person
137 grid-institutes:grid.411655.2 schema:alternateName Department of Communication Engineering, Chung-Hua University, 707, Sec 2, Wu-Fu Rd, 30012, Hsin-Chu, Taiwan, ROC
138 Ph. D. Program in Engineering Science, College of Engng., Chung-Hua University, 707, Sec 2, Wu-Fu Rd, 30012, Hsin-Chu, Taiwan, ROC
139 schema:name Department of Communication Engineering, Chung-Hua University, 707, Sec 2, Wu-Fu Rd, 30012, Hsin-Chu, Taiwan, ROC
140 Ph. D. Program in Engineering Science, College of Engng., Chung-Hua University, 707, Sec 2, Wu-Fu Rd, 30012, Hsin-Chu, Taiwan, ROC
141 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...