scholarly journals Developing a Novel Recurrent Neural Network Architecture with Fewer Parameters and Good Learning Performance

2020 ◽  
Author(s):  
Kazunori D YAMADA ◽  
Fangzhou LIN ◽  
Tsukasa NAKAMURA
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Learning Performance ◽  
Neural Network Architecture ◽  
Good Learning

scholarly journals Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network

2021 ◽  
Author(s):  
Zhan Li ◽  
Shuai Li
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Redundancy Resolution ◽  
Neural Network Architecture ◽  
Kinematic Control ◽  
Physical Constraints ◽  
Motion Constraints ◽  
Joint Limits ◽  
First Time

AbstractRedundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on a redundant manipulator.

Efficient Recurrent Neural Network Architecture for Musical Style Transfer

2021 ◽  
pp. 124-132
Author(s):  
Mateusz Modrzejewski ◽  
Konrad Bereda ◽  
Przemysław Rokita
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Musical Style ◽  
Neural Network Architecture ◽  
Style Transfer
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