1 簡(jiǎn)介

人工神經(jīng)網(wǎng)絡(luò)的最大缺點(diǎn)是訓(xùn)練時(shí)間太長(zhǎng)從而限制其實(shí)時(shí)應(yīng)用范圍,近年來(lái),極限學(xué)習(xí)機(jī)(Extreme Learning Machine, ELM)的提出使得前饋神經(jīng)網(wǎng)絡(luò)的訓(xùn)練時(shí)間大大縮短,然而當(dāng)原始數(shù)據(jù)混雜入大量噪聲變量時(shí),或者當(dāng)輸入數(shù)據(jù)維度非常高時(shí),極限學(xué)習(xí)機(jī)算法的綜合性能會(huì)受到很大的影響.深度學(xué)習(xí)算法的核心是特征映射,它能夠摒除原始數(shù)據(jù)中的噪聲,并且當(dāng)向低維度空間進(jìn)行映射時(shí),能夠很好的起到對(duì)數(shù)據(jù)降維的作用,因此我們思考利用深度學(xué)習(xí)的優(yōu)勢(shì)特性來(lái)彌補(bǔ)極限學(xué)習(xí)機(jī)的弱勢(shì)特性從而改善極限學(xué)習(xí)機(jī)的性能.為了進(jìn)一步提升DELM預(yù)測(cè)精度，本文采用麻雀搜索算法進(jìn)一步優(yōu)化DELM超參數(shù)，仿真結(jié)果表明，改進(jìn)算法的預(yù)測(cè)精度更高。

2 部分代碼

% --------------------------------------------------------------------%% Flower pollenation algorithm (FPA), or flower algorithm ? ? ? ? ? ? %% Programmed by Xin-She Yang @ May 2012 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? %% --------------------------------------------------------------------%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Notes: This demo program contains the very basic components of ? ? ?%% the flower pollination algorithm (FPA), or flower algorithm (FA), ? %% for single objective optimization. ? ?It usually works well for ? ? %% unconstrained functions only. For functions/problems with ? ? ? ? ? %% limits/bounds and constraints, constraint-handling techniques ? ? ? %% should be implemented to deal with constrained problems properly. ? %% ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? %% Citation details: ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? %%1)Xin-She Yang, Flower pollination algorithm for global optimization,%% Unconventional Computation and Natural Computation, ? ? ? ? ? ? ? ? %% Lecture Notes in Computer Science, Vol. 7445, pp. 240-249 (2012). ? %%2)X. S. Yang, M. Karamanoglu, X. S. He, Multi-objective flower ? ? ? %% algorithm for optimization, Procedia in Computer Science, ? ? ? ? ? %% vol. 18, pp. 861-868 (2013). ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%clcclear allclose alln=30; ? ? ? ? ? % Population size, typically 10 to 25p=0.8; ? ? ? ? ? % probabibility switch% Iteration parametersN_iter=3000; ? ? ? ? ? ?% Total number of iterationsfitnessMSE = ones(1,N_iter);% % Dimension of the search variables Example 1d=2;Lb = -1*ones(1,d);Ub = 1*ones(1,d);% % Dimension of the search variables Example 2% d=3;% Lb = [-2 -1 -1];% Ub = [2 1 1];%% % Dimension of the search variables Example 3% d=3;% Lb = [-1 -1 -1];% Ub = [1 1 1];%%% % % Dimension of the search variables Example 4% d=9;% Lb = -1.5*ones(1,d);% Ub = 1.5*ones(1,d);% Initialize the population/solutionsfor i=1:n, ? ?Sol(i,:)=Lb+(Ub-Lb).*rand(1,d); ? ?% To simulate the filters use fitnessX() functions in the next line ? ?Fitness(i)=fitness(Sol(i,:));end% Find the current best[fmin,I]=min(Fitness);best=Sol(I,:);S=Sol;% Start the iterations -- Flower Algorithmfor t=1:N_iter, ? ?% Loop over all bats/solutions ? ?for i=1:n, ? ? ? ?% Pollens are carried by insects and thus can move in ? ? ? ?% large scale, large distance. ? ? ? ?% This L should replace by Levy flights ? ? ? ?% Formula: x_i^{t+1}=x_i^t+ L (x_i^t-gbest) ? ? ? ?if rand>p, ? ? ? ? ? ?%% L=rand; ? ? ? ? ? ?L=Levy(d); ? ? ? ? ? ?dS=L.*(Sol(i,:)-best); ? ? ? ? ? ?S(i,:)=Sol(i,:)+dS; ? ? ? ? ? ?% Check if the simple limits/bounds are OK ? ? ? ? ? ?S(i,:)=simplebounds(S(i,:),Lb,Ub); ? ? ? ? ? ?% If not, then local pollenation of neighbor flowers ? ? ? ?else ? ? ? ? ? ?epsilon=rand; ? ? ? ? ? ?% Find random flowers in the neighbourhood ? ? ? ? ? ?JK=randperm(n); ? ? ? ? ? ?% As they are random, the first two entries also random ? ? ? ? ? ?% If the flower are the same or similar species, then ? ? ? ? ? ?% they can be pollenated, otherwise, no action. ? ? ? ? ? ?% Formula: x_i^{t+1}+epsilon*(x_j^t-x_k^t) ? ? ? ? ? ?S(i,:)=S(i,:)+epsilon*(Sol(JK(1),:)-Sol(JK(2),:)); ? ? ? ? ? ?% Check if the simple limits/bounds are OK ? ? ? ? ? ?S(i,:)=simplebounds(S(i,:),Lb,Ub); ? ? ? ?end ? ? ? ?% Evaluate new solutions ? ? ? ?% To simulate the filters use fitnessX() functions in the next ? ? ? ?% line ? ? ? ?Fnew=fitness(S(i,:)); ? ? ? ?% If fitness improves (better solutions found), update then ? ? ? ?if (Fnew<=Fitness(i)), ? ? ? ? ? ?Sol(i,:)=S(i,:); ? ? ? ? ? ?Fitness(i)=Fnew; ? ? ? ?end ? ? ? ?% Update the current global best ? ? ? ?if Fnew<=fmin, ? ? ? ? ? ?best=S(i,:) ? ; ? ? ? ? ? ?fmin=Fnew ? ; ? ? ? ?end ? ?end ? ?% Display results every 100 iterations ? ?if round(t/100)==t/100, ? ? ? ?best ? ? ? ?fmin ? ?end ? ?fitnessMSE(t) = fmin; end%figure, plot(1:N_iter,fitnessMSE);% Output/displaydisp(['Total number of evaluations: ',num2str(N_iter*n)]);disp(['Best solution=',num2str(best),' ? fmin=',num2str(fmin)]);figure(1)plot( fitnessMSE)xlabel('Iteration');ylabel('Best score obtained so far');

3 仿真結(jié)果

4 參考文獻(xiàn)

[1]馬萌萌. 基于深度學(xué)習(xí)的極限學(xué)習(xí)機(jī)算法研究[D]. 中國(guó)海洋大學(xué), 2015.

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