Application of neural networks in robotic control

The application of the fuzzy neural-logic network theory to improve the performance of controlling a robot is explored. Neural-logic is a three-valued logic and as such it can represent many more logical variations than the two-valued Boolean logic, e.g., the neural-logic network can implement the logical `NOT´ operation, which is essential for logical inference. It is concluded that the performance of a robot using the fuzzy neural-logic network controller will be significantly improved because it can handle the logical `DON´T KNOW´ operations so that it provides not only the conventional pattern matching capability, but also the inferencing capability