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Editorial

Neural clustering networks based on global optimisation of prototypes in metric spaces

Correlation structure of training data and the fitting ability of back propagation networks: Some experimental results

Computational neural networks for mapping calorimetric data: Application of feed-forward neural networks to kinetic parameters determination and signals filtering

Neuro-Resistive Grid approach to trainable controllers: A pole balancing example

A solution to the end-effector position optimisation problem in robotics using neural networks

Global optimisation in neural network training

Neuro-Resistive Grid approach to trainable controllers: A pole balancing example

Content Provider	Springer Nature Link
Author	Bapi, Raju S. D'Cruz, Brendan Bugmann, Guido
Copyright Year	1997
Abstract	A new neural network approach is described for the task of pole-balancing, considered a benchmark learning control problem. This approach combines Barto, Sutton and Anderson's [1] Associative Search Element (ASE) with a Neuro-Resistive Grid (NRG) [2] acting as Adaptive Critic Element (ACE). The novel feature in NRG is that it provides evaluation of a state based on propagation of the failure information to the neighbours in the grid. NRG is updated only on a failure, and provides ASE with a continuous internal reinforcement signal by comparing the value of the present state to the previous state. The resulting system learns more rapidly and with fewer computations than that of Barto et al.[1]. To establish a uniform basis of comparison of algorithms for pole balancing, both the systems are simulated using benchmark parameters and tests specified in Geva and Sitte [3].
Starting Page	33
Ending Page	44
Page Count	12
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	5
Issue Number	1
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	1997-01-01
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Inverted pendulum problem Reinforcement learning Learning control Nonlinear control Neural networks Neuro-resistive grid method Value map Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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