Neutral learning of constrained nonlinear transformations
- 1 June 1989
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Computer
- Vol. 22 (6) , 67-76
- https://doi.org/10.1109/2.30722
Abstract
Two issues that are fundamental to developing autonomous intelligent robots, namely rudimentary learning capability and dexterous manipulation, are examined. A powerful neural learning formalism is introduced for addressing a large class of nonlinear mapping problems, including redundant manipulator inverse kinematics, commonly encountered during the design of real-time adaptive control mechanisms. Artificial neural networks with terminal attractor dynamics are used. The rapid network convergence resulting from the infinite local stability of these attractors allows the development of fast neural learning algorithms. Approaches to manipulator inverse kinematics are reviewed, the neurodynamics model is discussed, and the neural learning algorithm is presented.<>Keywords
This publication has 10 references indexed in Scilit:
- Self-Organizing Neuromorphic Architecture for Manipulator Inverse KinematicsPublished by Springer Nature ,1991
- The least constraint principle for learning in neurodynamicsPhysics Letters A, 1989
- Terminal attractors for addressable memory in neural networksPhysics Letters A, 1988
- Neural-space generalization of a topological transformationBiological Cybernetics, 1988
- Solution to the inverse kinematics problem in robotics by neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988
- Generalization of back-propagation to recurrent neural networksPhysical Review Letters, 1987
- Parallel Distributed ProcessingPublished by MIT Press ,1986
- A complete generalized solution to the inverse kinematics of robotsIEEE Journal on Robotics and Automation, 1985
- Neurons with graded response have collective computational properties like those of two-state neurons.Proceedings of the National Academy of Sciences, 1984
- Uncertainty Analysis of Time-Dependent Nonlinear Systems: Theory and Application to Transient Thermal HydraulicsNuclear Science and Engineering, 1982