Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling

Fuzzy approach for data association in image tracking

A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets. It is embedded in an A-SMGCS Surveillance function for airport surface, based on video data processing, in charge of the automatic detection, identification and tracking of all interesting targets (aircraft and relevant ground vehicles). The tracking system captures a sequence of images, preprocesses them to extract the moving regions (blobs), and associates the blobs to tracks to estimate the number of targets in the scenario and their parameters. The system was initially built with a set of rules derived from performance analysis, and then a procedure based on neuro-fuzzy techniques was applied to automatically obtain rules from examples. A validation of learned system shows its capability to produce appropriate decisions. Results obtained with real data in representative ground operations show the system capabilities to solve complex scenarios and improve tracking accuracy.Peer Reviewe