Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

Kishan Kumar Kumbla

dblp:25/2213 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 1997
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 50% Planning, search and constraint satisfaction · 25% Robot manipulation · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › parameter identification
dynamics identification		 0.011997
Neural network based identification of robot dynamics used for neuro-fuzzy controller · ICRA 1997
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › intelligent control
neuro-fuzzy control		 0.011997
Neural network based identification of robot dynamics used for neuro-fuzzy controller · ICRA 1997
Robotics › Motion planning and robot control
robot control		 0.011997
Neural network based identification of robot dynamics used for neuro-fuzzy controller · ICRA 1997
Robotics › Motion planning and robot control
robot dynamics		 0.011997
Neural network based identification of robot dynamics used for neuro-fuzzy controller · ICRA 1997

Methods — techniques the papers use, named apart from their topics

neural network · 0.0fuzzy control · 0.0adaptive control · 0.0
YearPublicationVenuePosition
1997 Neural network based identification of robot dynamics used for neuro-fuzzy controller
abstract
A technique of identifying the dynamics of a robotics system using neural network is presented. The identified model is used by a fuzzy controller to evaluate the range of the control variables and also the performance of the adaptive control laws on the identified model. An overview of the neuro-fuzzy control architecture is also discussed. This architecture uses two neural networks, one which identifies the system dynamics and another classifies the temporal response of the robotic system. The information from the neural networks is used to make suitable adjustments in the parameter of the fuzzy controller. This paper however concentrates on the theory and operation of identifying the dynamics of a Adept-Two industrial robot. Simulation results are presented.
Kishan Kumar Kumbla, Mo Jamshidi 0001
 ICRA1