[lssvm regression prediction] Optimizing the least squares support vector machine SO-lssvm based on the snake swarm algorithm to implement data regression prediction with matlab code

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Content introduction

In the field of machine learning, Support Vector Machine (SVM) is a commonly used supervised learning method and is widely used in classification and regression problems. However, the traditional SVM model has some limitations when dealing with regression problems, such as being sensitive to noise and difficult to determine the model complexity. In order to overcome these problems, researchers have proposed a regression model based on the Least Squares Support Vector Machine (LSSVM).

The LSSVM regression model finds the best hyperplane by minimizing the objective function to achieve regression prediction of the data. However, due to the complexity of the objective function and the distribution of data in high-dimensional space, traditional optimization algorithms often perform poorly when solving LSSVM models. In order to further improve the performance of the LSSVM model, researchers introduced the Snake Optimization Algorithm (SO) to optimize the LSSVM model.

The snake swarm algorithm is an optimization algorithm based on the behavior of snake swarms in nature. It has the advantages of global search capability and fast convergence speed. By simulating the foraging behavior and predatory behavior of snake swarms, the snake swarm algorithm can find the optimal solution in the search space. In LSSVM regression prediction, the snake swarm algorithm can improve the model’s fitting ability and generalization performance by adjusting model parameters and optimizing the objective function.

In practical applications, the LSSVM model optimized based on the snake swarm algorithm performs well in data regression prediction. First of all, the snake swarm algorithm can globally search for optimal solutions, avoiding the problem of traditional optimization algorithms falling into local optimal solutions. Secondly, the snake swarm algorithm has a faster convergence speed and can find the optimal solution in a shorter time. Finally, the snake swarm algorithm can improve the prediction accuracy and generalization performance of the model by adjusting model parameters and optimizing the objective function.

However, there are also some challenges and limitations in the LSSVM model optimized based on the snake swarm algorithm. First of all, the performance of the snake swarm algorithm is affected by the initial parameter settings and the number of iterations, and reasonable parameter adjustment and optimization are required. Secondly, the snake swarm algorithm may face the problem of high computational complexity when processing large-scale data. Therefore, in practical applications, appropriate algorithm selection and parameter adjustment need to be made based on specific problems.

Part of the code

function [model,Yt] = prelssvm(model,Xt,Yt)</code><code>% Preprocessing of the LS-SVM</code><code>%</code><code>% These functions should only be called by trainlssvm or by </code><code>% simlssvm. At first the preprocessing assigns a label to each in-</code><code>% and output component (c for continuous, a for categorical or b </code><code>% for binary variables). According to this label each dimension is rescaled:</code><code>% </code><code>% * continuous: zero mean and unit variance</code> <code>% * categorical: no preprocessing</code><code>% * binary: labels -1 and + 1</code><code>% </code><code>% Full syntax (only using the object oriented interface):</code><code>% </code><code>% >> model = prelssvm(model)</code><code>% >> Xp = prelssvm(model, Xt)</code><code>% >> [empty, Yp] = prelssvm(model, [], Yt)</code><code>% >> [Xp, Yp] = prelssvm(model, Xt, Yt)</code><code>% </code><code>% Outputs </code><code>% model : Preprocessed object oriented representation of the LS-SVM model </code><code>% Xp : Nt x d matrix with the preprocessed inputs of the test data</code><code>% Yp : Nt x d matrix with the preprocessed outputs of the test data</code><code>% Inputs </code><code>% model : Object oriented representation of the LS-SVM model</code><code>% Xt : Nt x d matrix with the inputs of the test data to preprocess</code><code>% Yt : Nt x d matrix with the outputs of the test data to preprocess</code><code>% </code><code>% </code><code>% See also:</code><code>% postlssvm, trainlssvm</code><code>?</code><code>% Copyright (c) 2011, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.be/sista/lssvmlab</code><code>?</code><code>if model. preprocess(1)~='p', % no 'preprocessing</code><code> if nargin>=2, model = Xt; end </code><code> return</code><code>end</code><code>?</code><code>?</code><code>% </code><code>% what to do</code><code>% </code><code>if model .preprocess(1)=='p', </code><code> eval('if model.prestatus(1)==''c'',model.prestatus=''unschemed'';end',' model.prestatus=''unschemed'';');</code><code>end </code><code>?</code><code>?</code><code>if nargin==1, % only model rescaling </code><code> %</code><code> % if UNSCHEMED, redefine a rescaling</code><code> %</code><code> if model.prestatus(1)== 'u',% 'unschemed'</code><code> ffx =[];</code><code> </code><code> </code><code> for i=1:model.x_dim, </code><code> eval('ffx = [ffx model.pre_xscheme(i)];',...</code><code> 'ffx = [ffx signal_type(model.xtrain(:,i), inf)];');</code><code> end</code><code> model.pre_xscheme = ffx;</code><code> </code><code> ff = [];</code><code> for i=1:model.y_dim,</code><code> eval('ff = [ff model.pre_yscheme(i)];',...</code><code> 'ff = [ff signal_type(model.ytrain(:,i),model.type)];');</code><code> end</code><code> model.pre_yscheme = ff;</code><code> model.prestatus='schemed';</code><code> end</code><code> </code><code> %</code><code> % execute rescaling as defined if not yet CODED</code><code> %</code><code> if model.prestatus(1)=='s',% 'schemed' </code><code> model=premodel(model); </code><code> model.prestatus = 'ok';</code><code> end</code><code> </code><code> %</code><code> % rescaling of the to simulate inputs</code><code> %</code><code>elseif model.preprocess(1)=='p'</code><code> if model.prestatus(1)=='o',%'ok' </code> <code> eval('Yt;','Yt=[];');</code><code> [model,Yt] = premodel(model,Xt,Yt);</code><code> else </code><code> warning('model rescaling inconsistent..redo ''model=prelssvm(model);''..');</code><code> end</code><code>end</code><code>?</code><code>?</code><code>?</code><code>?</code><code>?</code><code>function [type,ss] = signal_type(signal,type)</code><code>%</code><code>% determine the type of the signal,</code><code>% binary classifier ('b'), categorical classifier (' a'), or continuous</code><code>% signal ('c')</code><code>%</code><code>%</code><code>ss = sort(signal); </code><code>dif = sum(ss(2:end)~=ss(1:end-1)) + 1;</code><code>% binary</code><code>if dif= =2,</code><code> type = 'b';</code><code>?</code><code>% categorical</code><code>elseif dif<sqrt(length(signal)) || type(1)== 'c',</code><code> type='a';</code><code>?</code><code>% continu</code><code>else </code><code> type ='c';</code><code>end</code><code> </code><code>?</code><code>?</code><code>?</code><code>?</code><code>%</code><code>% effective rescaling</code><code>%</code><code>function [model,Yt] = premodel(model,Xt,Yt)</code><code>%</code><code>%</code><code>%</code><code>?</code><code>if nargin= =1,</code><code>?</code><code> for i=1:model.x_dim,</code><code> % CONTINUOUS VARIABLE: </code><code> if model.pre_xscheme( i)=='c',</code><code> model.pre_xmean(i)=mean(model.xtrain(:,i));</code><code> model.pre_xstd(i) = std( model.xtrain(:,i));</code><code> model.xtrain(:,i) = pre_zmuv(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i) );</code><code> % CATEGORICAL VARIBALE: </code><code> elseif model.pre_xscheme(i)=='a',</code><code> model.pre_xmean(i)= 0;</code><code> model.pre_xstd(i) = 0;</code><code> model.xtrain(:,i) = pre_cat(model.xtrain(:,i),model.pre_xmean(i),model .pre_xstd(i));</code><code> % BINARY VARIBALE: </code><code> elseif model.pre_xscheme(i)=='b', </code><code> model.pre_xmean(i ) = min(model.xtrain(:,i));</code><code> model.pre_xstd(i) = max(model.xtrain(:,i));</code><code> model.xtrain (:,i) = pre_bin(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i));</code><code> end </code><code> end</code> code><code> </code><code> for i=1:model.y_dim,</code><code> % CONTINUOUS VARIABLE: </code><code> if model.pre_yscheme(i)=='c ',</code><code> model.pre_ymean(i)=mean(model.ytrain(:,i),1);</code><code> model.pre_ystd(i) = std(model.ytrain( :,i),1);</code><code> model.ytrain(:,i) = pre_zmuv(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i)); </code><code> % CATEGORICAL VARIBALE: </code><code> elseif model.pre_yscheme(i)=='a', </code><code> model.pre_ymean(i)=0;</code><code> model.pre_ystd(i) =0;</code><code> model.ytrain(:,i) = pre_cat(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd (i));</code><code> % BINARY VARIBALE: </code><code> elseif model.pre_yscheme(i)=='b', </code><code> model.pre_ymean(i) = min(model.ytrain(:,i));</code><code> model.pre_ystd(i) = max(model.ytrain(:,i));</code><code> model.ytrain(: ,i) = pre_bin(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i));</code><code> end </code><code> end</code> <code>?</code><code>else %if nargin>1, % testdata Xt, </code><code> if ~isempty(Xt),</code><code> if size(Xt,2) ~=model.x_dim, warning('dimensions of Xt not compatible with dimensions of support vectors...');end</code><code> for i=1:model.x_dim,</code><code> % CONTINUOUS VARIABLE: </code><code> if model.pre_xscheme(i)=='c',</code><code> Xt(:,i) = pre_zmuv(Xt(:,i),model.pre_xmean( i),model.pre_xstd(i));</code><code> % CATEGORICAL VARIBALE: </code><code> elseif model.pre_xscheme(i)=='a',</code><code> Xt (:,i) = pre_cat(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i));</code><code> % BINARY VARIBALE: </code><code> elseif model .pre_xscheme(i)=='b', </code><code> Xt(:,i) = pre_bin(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i));</code><code> end </code><code> end</code><code> end</code><code> </code><code> if nargin>2 & amp; ~isempty(Yt), </code><code> if size(Yt,2)~=model.y_dim, warning('dimensions of Yt not compatible with dimensions of training output...');end</code><code> for i= 1:model.y_dim,</code><code> % CONTINUOUS VARIABLE: </code><code> if model.pre_yscheme(i)=='c',</code><code> Yt(:,i) = pre_zmuv(Yt(:,i),model.pre_ymean(i), model.pre_ystd(i));</code><code> % CATEGORICAL VARIBALE: </code><code> elseif model.pre_yscheme(i) =='a', </code><code> Yt(:,i) = pre_cat(Yt(:,i),model.pre_ymean(i),model.pre_ystd(i));</code><code> % BINARY VARIBALE: </code><code> elseif model.pre_yscheme(i)=='b', </code><code> Yt(:,i) = pre_bin(Yt(:,i),model. pre_ymean(i),model.pre_ystd(i));</code><code> end</code><code> end</code><code> end</code><code> </code><code> % assign output</code><code> model=Xt;</code><code>end</code><code>?</code><code>?</code><code>function X = pre_zmuv (X,mean,var)</code><code>%</code><code>% preprocessing a continuous signal; rescaling to zero mean and unit</code><code>% variance </code><code> % 'c'</code><code>%</code><code>X = (X-mean)./var;</code><code>?</code><code>?</code> <code>function X = pre_cat(X,mean,range)</code><code>%</code><code>% preprocessing a categorical signal;</code><code>% 'a'</code> <code>%</code><code>X=X;</code><code>?</code><code>?</code><code>function X = pre_bin(X,min,max)</code><code>%</code><code>% preprocessing a binary signal;</code><code>% 'b'</code><code>%</code><code>if ~sum( isnan(X)) >= 1 %--> OneVsOne encoding</code><code> n = (X==min);</code><code> p = not(n);</code><code> X=-1.*(n) + p;</code><code>end</code><code>?</code><code>?</code><code>?</code><code>?

Operation results

References

[1] Sun Fengchao. Nonlinear predictive control based on least squares support vector machine [D]. China University of Petroleum [2023-09-28]. DOI: 10.7666/d.y1709445.

[2] Yang Zhao, Lu Chaofan, Liu Anli. Surface roughness prediction model and application based on PSO-LSSVM algorithm [J]. Machine Tools and Hydraulics, 2021, 49(6):5.

[3] Liu Yun, Yi Song. Optimization research on wind power time series prediction model based on two-parameter least squares support vector machine (TPA-LSSVM) [J]. Journal of Beijing University of Chemical Technology: Natural Science Edition, 2019, 46(2 ):6.DOI:CNKI:SUN:BJHY.0.2019-02-015.

[4] Yin Yue. Day-ahead photovoltaic power prediction based on particle swarm algorithm least squares support vector machine [J]. Distributed Energy, 2021, 6(2):7.DOI:10.16513/j.2096-2185.DE. 2106019.

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Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
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