ROBUST MORPHOLOGICALLY BASED SAMPLING FOR ANN

THE PROBLEM OF INTEREST IS TO IMPROVE PATTERN RECOGNITION TECHNIQUES BY DECREASING THE TRAINING TIME FOR ARTIFICIAL NEURAL NETWORKS (ANN) AND INCREASING THE ROBUTNESS OF THE RECALL OF ANN. WE PROPOSE TO DECREASE TRAINING TIME BY INTRODUCING MORPHOLOGICALLY BASED RECONFIGURABLE SAMPLING ARRAYS. WE WILL INCREASE THE FAULT TOLERANCE OR ROBUSTNESS OF OBJECT DETECTION TASKS BY INTRODUCING A HYBIRD MODEL THAT INCORPORATES ROBUST FILTERING PRIOR TO THE ANN INPUT. THERE ARE TWO PROBLEMS IN THIS AREA WE WOULD LIKE TO INVESTIGATE. AN OBVIOUS STEP IN DECREASING TRAINING TIME IS TO REDUCE THE NUMBER OF INPUTS AND THEREFORE THE NUMBER OF INTERCONNECTS. WE WILL INVESTIGATE THE EFFECTIVENESS OF THE INTERCONNECTION OF SENSORS ON MORPHOLOGICAL "SHAPING" CONSIDERATIONS AND PRIOR KNOWLEDGE OF THE OBJECT OF INTEREST. THE SECOND PROBLEM WE WISH TO INVESTIGATE IS AN INCREASE IN THE ROBUTNESS OF ANNS FOR BOTH IMPULSIVE NOISE AND VARYING SENSOR OUTPUT AMPLITUDES DUE TO VARYING INPUT INTENSITIES. WE WILL EXAMINE IMPULSIVE NOISE VIA THE USE OF ORDER STATISTIC (OS) FILTERS SUCH AS THE MEDIAN AFTER AN MORPHOLOGICALLY BASED INTERCONNTECTION OF THE SENSORS. WE WILL APPROACH THE PROBLEM OF VARYING INTENSITIES BY RENORMALIZATION OF THE INPUTS FROM THE MORPHOLOGICALLY SELECTED SET OF SENSORS TO THE ANN.