Name
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. |
Year | DOI | Venue |
|---|---|---|
1994 | 10.1016/b978-0-08-042236-7.50031-6 | AIRTC |
Keywords | DocType | Volume |
control system design,formal modeling,formal languages,neural nets,target tracking,stochastic systems,control,logic design | Conference | 19 |
ISSN | Citations | PageRank |
Annual Review in Automatic Programming | 2 | 0.57 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J.F Peters | 1 | 2 | 0.57 |
| L. Baumela | 2 | 15 | 1.59 |
| D. Maravall | 3 | 2 | 1.24 |
| S. Ramanna | 4 | 92 | 18.42 |