An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division

Algorithm 1

Kernel holistic learning and kernel interior sample generation.

	Initialization;
	for % h is the number of pattern categories
	for
	Count the number of initial samples belonging to the pattern category covered by each RBF hidden node;
	Use (8) to generate a sample set and count the number of generated samples ;
	for % Screening of generated samples according to the density
	Use (9) to estimate the probability density belonging to the current pattern category;
	;
	if
	;
	end if
	end for
	update;
	end for
	for %further screening of the overlapping region samples
	for
	if
	Use (10) to estimate the probability density belonging to the pattern category;
	if
	;
	end if
	end if
	end for
	end for
	end for