PROCEEDINGS ARTICLE | April 12, 2004

Proc. SPIE. 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004

KEYWORDS: Detection and tracking algorithms, Data modeling, Matrices, Pattern recognition, Sensor fusion, Optimization (mathematics), Tolerancing, Process modeling, Neurons, Limbic system

The solution of difficult optimization problems often requires the use of a parameter set allowing critical algorithm
design choices to be set. For example, in the construction of a valid pattern recognition scheme using a simple
feed forward network (FFN) technique, there can be thousands of equally valid FFN solutions which achieve
high percentage recognition levels on reasonable inputs. The solutions arise from different choices of stopping
tolerance, internal neuron architecture, learning rates and so forth. These meta level optimization parameter
choices can be used to organize collections of optimization algorithms into matrices W. Each column of the matrix
corresponds to a set of parameter choices such a stopping tolerance, learning rate, random restart choices and so
forth. For example, an optimization algorithm is constructed from a 4 x 3 matrix W by choosing an entry from
each column to construct a sequence ABC. The sequence ABC then encodes the collection of meta parameters
that are used to shape the algorithm. In this example, there are thus 64 possible optimization algorithms all
chosen to produce a similar output such as recognition rate. A simplified biologically based model of information
processing includes primary sensory processing and sensor fusion with construction of higher level meta data
modeled via recurrent connections between the site of sensor fusion and a simple model of limbic processing. We
illustrate how such a model can be constructed using as training data the matrices described above. Finally, the
use of this model to model the decision process is discussed.