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00731nam ac200229 k 4500
000003580457
20220101120000
ta
010731s1990 US 000 eng
▼a 0262132613
▼a 211070
▼c 211070
▼d 123456
▼l WM0003025786
▼l WM4623
▼c 2
▼a TJ223.M53
▼a TJ223.M53
▼b N48
▼a W THOMAS,MILLER
▼a NEURAL NETWORKS FOR CONTROL/
▼d MILLER,W THOMAS;
▼e Sutton, Richard S.;
▼e Werbos, Paul J.;
▼e National Science Foundation (U.S.)
▼a CAMBRIDGE:
▼b MIT PR.,
▼c 1990.
▼a 524p.;
▼c 24cm.
▼a Automatic control
▼a Congresses
▼a Neural computers
▼a Richard S.,Sutton
▼a Paul J.,Werbos
▼a National Science Foundation (U.S.)
▼a 단행본
| 자료유형 : | 단행본 |
|---|---|
| ISBN : | 0262132613 |
| 분류기호 : | TJ223.M53 |
| 개인저자 : | W THOMAS,MILLER |
| 서명/저자사항 : | NEURAL NETWORKS FOR CONTROL/ MILLER,W THOMAS; Sutton, Richard S.; Werbos, Paul J.; National Science Foundation (U.S.) |
| 발행사항 : | CAMBRIDGE: MIT PR., 1990. |
| 형태사항 : | 524p.; 24cm. |
| 개인저자 : | Richard S.,Sutton |
| 개인저자 : | Paul J.,Werbos |
| 개인저자 : | National Science Foundation (U.S.) |
| 언어 | 영어 |
WMO199925752
권 호 : 524
발행년 : 1990
서 명 : NEURAL NETWORKS FOR CONTROL
발행처 : MILLER,W THOMAS
목차
1. CONNECTIONIST LEARNING FOR CONTROL
2. OVERVIEW OF DESIGNS AND CAPABILITIES
3. A MENU OF DESIGNS FOR REINFORCEMENT LEARNING OVER TIME
4. ADAPTIVE STATE REPRESENTATION AND ESTIMATION USING RECURRENT CONNEC-
5. TIONIST NETWORKS
6. ADAPTIVE CONTROL USING NEURAL NETWORKS
7. A SUMMARY COMPARISON OF CMAC NEURAL NETWORK AND TRADITIONAL ADAPTIVE
8. CONTROL SYSTEMS
9. RECENT ADVANCES IN NUMERICAL TECHNIQUES FOR LARGE SCALE OPTIMIZATION
10. FIRST RESULTS WITH DYNA,AN INTERGRATED ARTCHITECTURE FOR LEARING,
11. PLANNING AND REACTING
12. COMPUTATIONAL SCHEMES AND NEURAL NETWORK MODELS FOR FORMATION AND
13. CONTROL OF MULTIHOINT ARM TRAJECTORY
14. VISION-BASED ROBOT MOTION PLANNING
15. USING ASSOCIATIVE CONTENT-ADDRESSABLE MEMORIES TO CONTROL ROBOTS
16. THE TRUCK BACKER-UPPER;AN EXAMPLE OF SELF-LEARNING IN NEURAL NETWORKS
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