Linear Hopfield networks, inverse kinematics and constrained optimization

Author

Mathia, Karl ; Saeks, Richard ; Lendaris, George G.

Author_Institution

Accurate Autom. Corp., Chattanooga, TN, USA

Volume

2

fYear

1994

fDate

2-5 Oct 1994

Firstpage

1269

Abstract

Methods for the design of different types of linear Hopfield networks are presented. The resulting neural networks are guaranteed to converge to their stable equilibrium, i.e. to solutions of the linear equations implicitly represented by the network. The construction of a step size is introduced, which allows convergence of the dynamic process at or near maximum rate. This work is a continuation the authors´ previous work (1994), and as an application example a neural network solution to the inverse kinematics problem is described

Keywords

Hopfield neural nets; convergence; inverse problems; kinematics; optimisation; stability; constrained optimization; inverse kinematics; inverse kinematics problem; linear Hopfield neural networks; linear equations; stable equilibrium; Constraint optimization; Control systems; Design methodology; Equations; Kinematics; Least squares methods; Neural networks; Orbital robotics; Recurrent neural networks; Robot sensing systems; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-2129-4

Type

conf

DOI

10.1109/ICSMC.1994.400019

Filename

400019