Constructive Neuron Networks Classification Algorithm Based on Biomimetic Pattern Recognition

In this paper, based on Biomimetic Pattern Recognition theory, a Constructive Neuron Networks Classification Algorithm is proposed. The theory believes that: There exists at least one gradual change course between two things and all the things in this gradual change course belong to the same class, if these two things belong to the same class. Analyzing the geometry meaning of different structure neurons in high dimensional space, high dimensional geometrical distribution of the sample set in the feature space can be covered by constructing a new type of ANN. The high recognition rate of the double screw curves experiment has proved the validity of the new algorithm.