A Synergetic classification algorithm based on prototype modify  with particle swarm optimization measure

The synergetic pattern recognition is a new way of pattern recognition with many excellent features such as noise resistance, deformity resistance, and better robustness. the selection of prototype patterns is very important to pattern recognition of synergetic approach, which set the tone for the recognition performance of synergetic approach. the superposition modify of information is better in the existing methods of prototype selection, prototype modify method with particle swarm optimization measure is applied to avoided information saturation,and get the optimal prototype experiment result on Brodatz texture images and nasopharyngeal carcinoma cell images shows that the new algorithm can effectively search the optimal prototype patterns, the synergetic recognition method proposed in this paper is more available than classical synergetic pattern recognition method, and excellent, correct and fast recognition result has been achieved, with good potential clinical application