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Neural network – support vector machine

Support Vector Machine (SVM) was first proposed by Cortes and Vapnik in 1995. It shows many unique advantages in solving small sample size, nonlinear and high-dimensional pattern recognition, and can be generalized to other Machine learning problems such as function fitting. 1 Mathematics section 1.1 Two-dimensional space​​​ ​​​ ​​​ ​​​ ​​​ ​​​ ​​​ ​​​ ​2 algorithm Part​​​ ​​​ ​​​ ​

Second, the seagull algorithm

Seagull algorithm mainly simulates the migration behavior and attack behavior of seagulls. Migration behavior is that seagulls fly from one place which is not suitable for survival at the present stage to another place which is suitable for survival. Migration behavior affects the global exploration ability of SOA algorithm. Aggressive behavior refers to the aggressive foraging of seagulls for food on the ground and in water during flight, which affects the local development ability of SOA algorithms.

2. SOA algorithm flow

2.1 Migration (Exploration ability)

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2.2 Exploitation Ability

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2.3 SOA algorithm process

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Three, code,

clear all 
clc
SearchAgents=30; 
Fun_name='F1';  
Max_iterations=1000; 
[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name);
[Best_score,Best_pos,SOA_curve]=soa(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness);


figure('Position',[500 500 660 290])

subplot(1,2,1);
func_plot(Fun_name);
title('Objective space')
xlabel('x_1');
ylabel('x_2');
zlabel([Fun_name,'( x_1 , x_2 )'])

subplot(1,2,2);
plots=semilogx(SOA_curve,'Color','r');
set(plots,'linewidth',2)
hold on
title('Objective space')
xlabel('Iterations');
ylabel('Best score');

axis tight
grid on
box on
legend('SOA')

display(['The best solution obtained by SOA is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by SOA is : ', num2str(Best_score)]);

        
Copy the code

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5. References:

The book “MATLAB Neural Network 43 Case Analysis”

Complete code download or simulation consulting www.cnblogs.com/ttmatlab/p/…

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