Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning

Table 3

Initial parameters used in learning algorithms.

Algorithm	Parameter	Value
BP	Learning rate	0.01
	Maximum number of generations	1000
BBO	Habitat modification probability	1
	Immigration probability bounds per gene	[0, 1]
	Step size for numerical integration of probabilities	1
	Max immigration and max emigration	1
	Mutation probability	0.005
	Population size	50
	Maximum number of generations	1000
PSO	Acceleration constants	[2]
	Inertia weights	[0.9, 0.5]
	Population size	50
	Maximum number of generations	1000
GA	Selection mechanism	Roulette wheel
	Crossover probability	0.9
	Mutation probability	0.1
	Population size	50
	Maximum number of generations	1000
PBIL	Learning rate	0.05
	Good population member	1
	Bad population member	0
	Elitism parameter	1
	Mutational probability	0.1
	Population size	50
	Maximum number of generations	1000
CSO	The swarm size m
	The social factor φ
	Population size	50
	Maximum number of generations	1000
DE	Crossover probability	0.9
	Differential weight	0.5
	Population size	50
	Maximum number of generations	1000
jSO	Mutation coefficient MF	0.3
	Crossover probability MCR	0.5
	Historical memory size H	5
	Population size	50
	Maximum number of generations	1000