1 簡介

BP神經(jīng)網(wǎng)絡(luò)算法使用非常廣泛,傳統(tǒng)的BP神經(jīng)網(wǎng)絡(luò)算法雖然具有不錯(cuò)的擬合非線性函數(shù)的能力,但是容易陷入局部的極小值,并且傳統(tǒng)的算法收斂的速度慢.本篇文章詳細(xì)地論述了如何使用ent混沌映射原子搜索算法算法優(yōu)化傳統(tǒng)的BP神經(jīng)網(wǎng)絡(luò)算法中初始的權(quán)值和閥值,通過相應(yīng)的驗(yàn)證和比較提出了該模型的有效性.

作為物理-元啟發(fā)式算法中的一種，ASO 最早在 2018 年由趙衛(wèi)國提出并將其應(yīng)用于地下水分散系數(shù)估計(jì)。ASO 的靈感來自于基本的分子動(dòng)力學(xué),自然界中所有的物質(zhì)都是由原子組成,原子具備質(zhì)量和體積,在一個(gè)原子系統(tǒng)中,所有原子都是相互作用并且處于恒定的運(yùn)動(dòng)狀態(tài)，其微觀相互作用十分復(fù)雜。隨著科學(xué)技術(shù)的發(fā)展，近些年來分子動(dòng)力學(xué)發(fā)展迅速，已經(jīng)可以使用計(jì)算機(jī)模擬原子和分子的物理運(yùn)動(dòng)規(guī)律。

2 部分代碼

%--------------------------------------------------------------------------% GSA code v1.0.% Developed in MATLAB R2011b% The code is based on the following papers.% W. Zhao, L. Wang and Z. Zhang, Atom search optimization and its % application to solve a hydrogeologic parameter estimation problem, % Knowledge-Based Systems (2018), https://doi.org/10.1016/j.knosys.2018.08.030.%% W. Zhao, L. Wang and Z. Zhang, A novel atom search optimization for % dispersion coefficient estimation in groundwater, Future Generation % Computer Systems (2018), https://doi.org/10.1016/j.future.2018.05.037.%--------------------------------------------------------------------------% Atom Search Optimization.function [X_Best,Fit_XBest,Functon_Best]=ASO(alpha,beta,Fun_Index,Atom_Num,Max_Iteration)% Dim: Dimension of search space.% Atom_Pop: Population (position) of atoms.% Atom_V: ?Velocity of atoms.% Acc: Acceleration of atoms.% M: Mass of atoms. % Atom_Num: Number of atom population.% Fitness: Fitness of atoms.% Max_Iteration: Maximum of iterations.% X_Best: Best solution (position) found so far. % Fit_XBest: Best result corresponding to X_Best. % Functon_Best: The fitness over iterations. % Low: The low bound of search space.% Up: The up bound of search space.% alpha: Depth weight.% beta: Multiplier weightalpha=50;beta=0.2; ? Iteration=1; ? [Low,Up,Dim]=Test_Functions_Range(Fun_Index); ? % Randomly initialize positions and velocities of atoms. ? ? if size(Up,2)==1 ? ? ? ? Atom_Pop=rand(Atom_Num,Dim).*(Up-Low)+Low; ? ? ? ? Atom_V=rand(Atom_Num,Dim).*(Up-Low)+Low; ? ? end ? ? if size(Up,2)>1 ? ? ? ?for i=1:Dim ? ? ? ? ? Atom_Pop(:,i)=rand(Atom_Num,1).*(Up(i)-Low(i))+Low(i); ? ? ? ? ? Atom_V(:,i)=rand(Atom_Num,1).*(Up(i)-Low(i))+Low(i); ? ? ? ?end ? ? end % Compute function fitness of atoms. ? ? for i=1:Atom_Num ? ? ? Fitness(i)=Test_Functions(Atom_Pop(i,:),Fun_Index,Dim); ? ? end ? ? ? Functon_Best=zeros(Max_Iteration,1); ? ? ? [Max_Fitness,Index]=min(Fitness); ? ? ? Functon_Best(1)=Fitness(Index); ? ? ? X_Best=Atom_Pop(Index,:); % Calculate acceleration. Atom_Acc=Acceleration(Atom_Pop,Fitness,Iteration,Max_Iteration,Dim,Atom_Num,X_Best,alpha,beta); % Iteration for Iteration=2:Max_Iteration ? ? ? ? ? Functon_Best(Iteration)=Functon_Best(Iteration-1); ? ? ? ? ? Atom_V=rand(Atom_Num,Dim).*Atom_V+Atom_Acc; ? ? ? ? ? Atom_Pop=Atom_Pop+Atom_V; ? ? ? ? ? ? for i=1:Atom_Num ? ? ? % Relocate atom out of range. ? ? ? ? ? ? TU= Atom_Pop(i,:)>Up; ? ? ? ? ? TL= Atom_Pop(i,:)<Low; ? ? ? ? ? Atom_Pop(i,:)=(Atom_Pop(i,:).*(~(TU+TL)))+((rand(1,Dim).*(Up-Low)+Low).*(TU+TL)); ? ? ? ? ? %evaluate atom. ? ? ? ? ? Fitness(i)=Test_Functions(Atom_Pop(i,:),Fun_Index,Dim); ? ? ? ? end ? ? ? ?[Max_Fitness,Index]=min(Fitness); ? ? ? ? ? ? ?if Max_Fitness<Functon_Best(Iteration) ? ? ? ? ? ? Functon_Best(Iteration)=Max_Fitness; ? ? ? ? ? ? X_Best=Atom_Pop(Index,:); ? ? ? ? ?else ? ? ? ? ? ?r=fix(rand*Atom_Num)+1; ? ? ? ? ? ? Atom_Pop(r,:)=X_Best; ? ? ? ?end ? ? ?% Calculate acceleration. ? ? ? Atom_Acc=Acceleration(Atom_Pop,Fitness,Iteration,Max_Iteration,Dim,Atom_Num,X_Best,alpha,beta); endFit_XBest=Functon_Best(Iteration);

3 仿真結(jié)果

4 參考文獻(xiàn)

[1]馬俊濤. 基于MATLAB的BP神經(jīng)網(wǎng)絡(luò)模型的預(yù)測(cè)算法研究[C]// 軍事信息軟件與仿真學(xué)術(shù)研討會(huì). 中國電子學(xué)會(huì), 2006.

博主簡介：擅長智能優(yōu)化算法、神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)、信號(hào)處理、元胞自動(dòng)機(jī)、圖像處理、路徑規(guī)劃、無人機(jī)等多種領(lǐng)域的Matlab仿真，相關(guān)matlab代碼問題可私信交流。

部分理論引用網(wǎng)絡(luò)文獻(xiàn)，若有侵權(quán)聯(lián)系博主刪除。