Logical design of neural controllers

Abstract

The logical design of a neural controller is achieved by representing a neural computation as a stochastic timed linear proof with a built-in system for rewards and punishments based on the timeliness of a computation performed by a neural controller. Logical designs are represented with stochastic forms of proofnets and proofboxes. Sample applications of the logical design methodology to the truck-backer upper and a Real-Time object recognition and tracking system (RTorts) are presented. Performance results of the implementation of the target dynamics identification module of the RTorts are given and compared to similar systems.