Publications & Demos
- CogniMem Decision Space Mapping
- CogniMem Technology Reference Guide
- Pattern Learning with CogniMem
- Pattern Recognition with CogniMem
- CogniMem and Multiple Experts
- Can it be Simpler to Program?
- Using RBF for Weather Prediction
- Success of the RBF Classifier for Character Recognition
- Download Executable RBF Tutorial
The architecture of the CogniMem™ chip makes it the most practical implementation of a Radial Basis Function classifier with autonomous adaptive learning capabilities.
The Radial Basis Function is a classifier capable of representing complex nonlinear decision spaces using hyperspheres with adaptable radii. It is widely used for face recognition and other image recognition applications, function approximation, time series prediction, novelty detection.
The CogniMem Advantage: Upon receipt of an input vector, all the cognitive memories holding a previously learned vector calculate their distance to the input vector and evaluate immediately if it falls in their similarity domain. If so, the “firing” cells are ready to output their response in an orderly fashion giving the way to the cell which holds the smallest distance. If no cell fires and a teaching command is issued, the next available cell automatically learns the vector. Also, if a teaching command conflicts with the category that a firing cell, the latter automatically corrects itself by reducing its influence field.
This autonomous learning and recognition behavior pertains to the unique CogniMem parallel architecture and a patented Search and Sort process.