The authors describe a multinetwork, or modular, neural network architecture that learns to
perform control tasks using a piecewise control strategy. The architecture's networks …

For recognition and control of multiple manipulated objects, we present two learning
schemes for neuralnetwork controllers based on feedback-error-learning and modular …

A mobile robot whose behavior is controlled by a structured hierarchical neural network and
its learning algorithm is presented. The robot has four wheels and moves about freely with …

The author presents a learning algorithm and capabilities of perceptron-like neural networks
whose outputs and inputs are directly connected to plants just like ordinary feedback …

In this appendix, we present a number of control problems as challenges to experimenters
wishing to explore new ideas for building automatic controllers that improve their …

A multilayered neural network processor is used to control a given plant. Several learning
architectures are proposed for training the neural controller to provide the appropriate inputs …

Dynamical systems are ubiquitous in nature and include naturally occurring systems such as
the cell and more complex biological organisms, the interactions of populations, and so on …

A practical neural network-based learning control system is described that is applicable to
complex robotic systems involving multiple feedback sensors and multiple command …

We present a strategy for learning optimal control. The approach uses functional-link neural
network implementations which have several beneficial properties giving advantages over …

Two important computational features of neural networks are associative storage and
retrieval of knowledge, and uniform rate of convergence of network dynamics independent …