Programming a massively parallel, computation universal system: Static behavior
AIP Conference Proceedings 151 on Neural Networks for Computing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Robot Manipulators: Mathematics, Programming, and Control
Robot Manipulators: Mathematics, Programming, and Control
Single-Iteration Training Algorithm for Multi-Layer Feed-Forward Neural Networks
Neural Processing Letters
On-line regression algorithms for learning mechanical models of robots: A survey
Robotics and Autonomous Systems
| Hi-index | 4.10 |
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.