【BP分类】基于头脑风暴算法优化BP神经网络实现数据分类附matlab代码
【BP分类】基于头脑风暴算法优化BP神经网络实现数据分类附matlab代码
TT_Matlab
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，完整matlab代码或者程序定制加qq1575304183。
1 简介
为了提高分类的准确性,该模型引入具有全局寻优特点的头脑风暴优化算法,用于模拟人类提出创造性思维解决问题的过程,具有强大的全局搜索和局部搜索的能力,同时利用BP神经网络所具有良好的非线性映射能力,学习适应能力和容错性,最大程度上考虑到海洋水质评价因素的非线性和非平稳的关系,得到BP神经网络的各层权值,阈值的最优解,实验结果表明该评价模型能够克服局部极小问题,分类结果准确性较高,并具有一定的实用性.
2 部分代码
function best_fitness = bso2(fun,n_p,n_d,n_c,rang_l,rang_r,max_iteration)
% fun = fitness_function
% n_p; population size
% n_d; number of dimension
% n_c: number of clusters
% rang_l; left boundary of the dynamic range
% rang_r; right boundary of the dynamic range
prob_one_cluster =
0
.
8
; % probability
for
select
one cluster to form new individual;
stepSize = ones(
1
,n_d); % effecting the step size of generating new individuals by adding random
values
popu = rang_l + (rang_r - rang_l) *
rand
(n_p,n_d); % initialize the population of individuals
popu_sorted = rang_l + (rang_r - rang_l) *
rand
(n_p,n_d); % initialize the population of individuals sorted according to clusters
n_iteration =
0
; % current iteration number
% initialize cluster probability to be zeros
prob = zeros(n_c,
1
);
best = zeros(n_c,
1
); %
index
of best individual in
each
cluster
centers = rang_l + (rang_r - rang_l) *
rand
(n_c,n_d); % initialize best individual in
each
cluster
centers_copy = rang_l + (rang_r - rang_l) *
rand
(n_c,n_d); % initialize best individual-COPY in
each
cluster FOR the purpose of introduce random best
best_fitness =
1000000
*ones(max_iteration,
1
);
fitness_popu =
1000000
*ones(n_p,
1
); % store fitness value
for
each
individual
fitness_popu_sorted =
1000000
*ones(n_p,
1
); % store fitness value
for
each
sorted individual
indi_temp = zeros(
1
,n_d); % store temperary individual
% calculate fitness
for
each
individual in the initialized population
for
idx =
1
:n_p
fitness_popu(idx,
1
) = fun(popu(idx,:));
end
while
n_iteration < max_iteration
cluster = kmeans(popu, n_c,
’Distance’
,
’cityblock’
,
’Start’
,centers,
’EmptyAction’
,
’singleton’
); % k-mean cluster
% clustering
fit_values =
100000000000000000000000000.0
*ones(n_c,
1
); % assign a initial big fitness value as best fitness
for
each
cluster in minimization problems
number_in_cluster = zeros(n_c,
1
); % initialize
0
individual in
each
cluster
for
idx =
1
:n_p
number_in_cluster(cluster(idx,
1
),
1
)= number_in_cluster(cluster(idx,
1
),
1
) +
1
;
% find the best individual in
each
cluster
if
fit_values(cluster(idx,
1
),
1
) > fitness_popu(idx,
1
) % minimization
fit_values(cluster(idx,
1
),
1
) = fitness_popu(idx,
1
);
best(cluster(idx,
1
),
1
) = idx;
end
end
% form population sorted according to clusters
counter_cluster = zeros(n_c,
1
); % initialize cluster counter to be
0
acculate_num_cluster = zeros(n_c,
1
); % initialize accumulated number of individuals in previous clusters
for
idx =
2
:n_c
acculate_num_cluster(idx,
1
) = acculate_num_cluster((idx-
1
),
1
) + number_in_cluster((idx-
1
),
1
);
end
%start form sorted population
for
idx =
1
:n_p
counter_cluster(cluster(idx,
1
),
1
) = counter_cluster(cluster(idx,
1
),
1
) +
1
;
temIdx = acculate_num_cluster(cluster(idx,
1
),
1
) + counter_cluster(cluster(idx,
1
),
1
);
popu_sorted(temIdx,:) = popu(idx,:);
fitness_popu_sorted(temIdx,
1
) = fitness_popu(idx,
1
);
end
% record the best individual in
each
cluster
for
idx =
1
:n_c
centers(idx,:) = popu(best(idx,
1
),:);
end
if
(
rand
() <
0
.
2
) %
select
one cluster center to be replaced by a randomly generated center
cenIdx = ceil(
rand
()*n_c);
centers(cenIdx,:) = rang_l + (rang_r - rang_l) *
rand
(
1
,n_d);
end
% calculate cluster probabilities based on number of individuals in
each
cluster
for
idx =
1
:n_c
prob(idx,
1
) = number_in_cluster(idx,
1
)/n_p;
if
idx >
1
prob(idx,
1
) = prob(idx,
1
) + prob(idx-
1
,
1
);
end
end
% generate n_p new individuals by adding Gaussian random
values
for
idx =
1
:n_p
r_1 =
rand
(); % probability
for
select
one cluster to form new individual
if
r_1 < prob_one_cluster %
select
one cluster
r =
rand
();
for
idj =
1
:n_c
if
r < prob(idj,
1
)
if
rand
() <
0
.
4
%
use
the center
indi_temp(
1
,:) = centers(idj,:);
else
%
use
one randomly selected cluster
indi_1 = acculate_num_cluster(idj,
1
) + ceil(
rand
() * number_in_cluster(idj,
1
));
indi_temp(
1
,:) = popu_sorted(indi_1,:);
end
break
end
end
else
%
select
two clusters
% pick two clusters
cluster_1 = ceil(
rand
() * n_c);
indi_1 = acculate_num_cluster(cluster_1,
1
) + ceil(
rand
() * number_in_cluster(cluster_1,
1
));
cluster_2 = ceil(
rand
() * n_c);
indi_2 = acculate_num_cluster(cluster_2,
1
) + ceil(
rand
() * number_in_cluster(cluster_2,
1
));
tem =
rand
();
if
rand
() <
0
.
5
%use center
indi_temp(
1
,:) = tem * centers(cluster_1,:) + (
1
-tem) * centers(cluster_2,:);
else
%
use
randomly selected individuals from
each
cluster
indi_temp(
1
,:) = tem * popu_sorted(indi_1,:) + (
1
-tem) * popu_sorted(indi_2,:);
end
end
stepSize = logsig(((
0
.
5
*max_iteration - n_iteration)/
20
)) *
rand
(
1
,n_d);
indi_temp(
1
,:) = indi_temp(
1
,:) + stepSize .* normrnd(
0
,
1
,
1
,n_d);
%
if
better than the previous one, replace it
fv = fun(indi_temp);
if
fv < fitness_popu(idx,
1
) % better than the previous one, replace
fitness_popu(idx,
1
) = fv;
popu(idx,:) = indi_temp(
1
,:);
end
end
% keep the best
for
each
cluster
for
idx =
1
:n_c
popu(best(idx,
1
),:) = centers_copy(idx,:);
fitness_popu(best(idx,
1
),
1
) = fit_values(idx,
1
);
end
n_iteration = n_iteration +
1
;
% record the best fitness in
each
iteration
best_fitness(n_iteration,
1
) = min(fit_values);
end
3 仿真结果
4 参考文献
[1]李海涛, 邵泽东. 基于头脑风暴优化算法与BP神经网络的海水水质评价模型研究[J]. 应用海洋学学报, 2020, 39(1):6.
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。
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