Skip to main navigation
Skip to search
Skip to main content
Okayama University Home
English
日本語
Home
Researcher Profiles
Research units
Research output
Activities
Press/Media
Prizes
Search by expertise, name or affiliation
Adaptive learning with large variability of teaching signals for neural networks and its application to motion control of an industrial robot
Fusaomi Nagata, Keigo Watanabe
Graduate School of Natural Science and Technology
Faculty of Engineering
Academic Field of Natural Science and Technology
Research output
:
Contribution to journal
›
Article
›
peer-review
13
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Adaptive learning with large variability of teaching signals for neural networks and its application to motion control of an industrial robot'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Industrial Robot
100%
Adaptive Learning
94%
Motion Control
90%
Compensator
71%
Teaching
58%
Feedforward
56%
Controller
54%
Neural Networks
53%
Centrifugal Force
44%
Coriolis Force
41%
Learning
38%
Manipulator
36%
Gravity
30%
Back-propagation Algorithm
23%
Trial and error
19%
Learning Process
19%
Recurrent Neural Networks
18%
Robotics
17%
Tuning
16%
Dynamic Model
15%
Friction
14%
Directly proportional
12%
Requirements
11%
Simulation
8%
Derivative
8%
Performance
8%
Coefficient
7%
Engineering & Materials Science
Industrial robots
63%
Motion control
58%
Teaching
50%
Neural networks
34%
Controllers
30%
Gravitation
25%
Manipulators
24%
Recurrent neural networks
14%
Backpropagation
13%
Derivatives
11%
Dynamic models
11%
Tuning
11%
Robotics
10%
Sampling
9%
Friction
9%