Modelling of parametrized processes via regression in the model space of neural networks

Aswolinskiy, Witali; Reinhart, René Felix; Steil, Jochen J.

doi:10.1016/j.neucom.2016.12.086

Cited by 8 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ELM specialist is responsible for the mapping to output torques corresponding to the input six joint angles. [18]. The generalist learner estimates the read-out weights for the specialist learner.…”

Section: F I N a L D R A F Tmentioning

confidence: 99%

Adaptive handling assistance for industrial lightweight robots in simulation

Balayn

Queiser

Wojtynek

et al. 2016

2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

View full text Add to dashboard Cite

Abstract-With the growth of lightweight robots in industry handling assistance becomes more and more important for solving industrial tasks. We present an adaptive compliance control mode for industrial robots based on a learned equilibrium model. This enables us to cope with changing manufacturing environments, attached grippers as well as devices with inaccurate dynamic models, e.g. stiff tubes, wires, protection shields or hose packages. A further feature of the proposed method is the expandability by an additional parameterization that allows to deal with task variability. In this work we evaluate our approach using the example of a task that incorporates variable payloads. All experiments are conducted in a simulation framework to evaluate the feasibility of the proposed approach for industrial robot scenarios.

show abstract

Section: F I N a L D R A F Tmentioning

confidence: 99%

Adaptive handling assistance for industrial lightweight robots in simulation

Balayn

Queiser

Wojtynek

et al. 2016

2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

View full text Add to dashboard Cite

show abstract

“…Input driven dynamical systems play a prominent role in machine learning as models applied to time series data, e.g. [2,10,21,15]. There has been a lively research activity on formulating and assessing different aspects of computational power and information processing in such systems (see e.g.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Fisher memory of randomized linear symmetric Echo State Networks

Tiňo

2018

Neurocomputing

View full text Add to dashboard Cite

Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. • Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

show abstract