Mathematical Problems in Engineering / 2019 / Article / Tab 1

Research Article

A Novel Nonlinear Function Fitting Model Based on FOA and GRNN

Table 1

Parameters setting of algorithms.

Model TypeTraining and Testing samplesAlgorithm parameters

FOA-GRNNTraining samples: 8000 dataset
Testing samples: 2000 dataset
Individual: the SPREAD parameter;
Individual fitness function: Sum of absolute errors;
Evolution generations:20;
Population size:20;

GA-BPSameIndividual: the weights and thresholds of BP network;
Individual fitness function: Same;
Evolution generations: Same;
Population size: Same;
Crossover probability: 0.2;
Mutation probability: 0.1;

PSO-BPSameIndividual: the weights and thresholds of BP network;
Individual fitness function: Same;
Evolution generations: Same;
Population size: Same;
Particle maximum:0.55;
Particle minimum:0.05;
Velocity maximum:1;
Velocity minimum: -1;
Acceleration constants c1& c2: 1.49445;

GRNNSameIndividual: Same;
Individual fitness function: Same;
Evolution generations: Same;
Population size: Same;

BPSameIndividual: the weights and thresholds of BP network;
Individual fitness function: Same;
Evolution generations: Same;
Population size: Same;