An intelligent algorithm of Support Vector Regression parameters optimization in soft measurements

Support vector regression with genetic algorithms in forecasting tourism demand

Forecasting systems reliability based on support vector regression with genetic algorithms

A dynamic particle filter-support vector regression method for reliability prediction

A novel support vector regression(SVR) was proposed for complex grouting process, which has the feature of invisible stratum and varied rock facture width. The features of SVR were selected through analyzing the major mechanism sub-models. In order to improve regression model performance, a fast embedded uniform algorithm of optimizing parameters of SVR was introduced. Two steps of optimization are adopted in the embedded uniform algorithm. Firstly rough parameters of SVR are found by throwing uniform point in rectangle area of parameters, seccondly the precise parameters of SVR are found in new subsection of grid. At last, 600 sets data have be selected to test in HENGYANG dam project of Hunan province in China. The results of simulation show that the embedded uniform method is superior to grid algorithm in arithmetic speed and precision in grouting model.

Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm

DSLC-FOA : Improved fruit fly optimization algorithm for application to structural engineering design optimization problems

To address the issues of local optima trapping and slow convergence in the Fruit Fly Optimization Algorithm (FOA), an enhanced approach called Membrane Computing-based Fruit Fly Optimization Algorithm (MCFOA) is proposed to optimize models. The MCFOA algorithm merges the parallelism of membrane computing with the efficient search capabilities of the fruit fly optimization algorithm, thereby enhancing both search accuracy and convergence speed. First, the improved fruit fly optimization algorithm was built by the cell-like P system consisting of three cells to find the optimal initial weights and thresholds for the BP neural network, to improve the accuracy of grain temperature prediction. In addition, the three cells used different fruit fly optimization algorithms as their evolutionary mechanisms. Under the control of evolutionary mechanisms and communication mechanisms, the model was applied to improve the convergence performance of the algorithm, overcome the defect of falling into local optimal, and continuously optimize the weights and thresholds of the BP neural network to achieve the aiming of improving the prediction accuracy. The results showed that the prediction curve of the MCFOA-BP model is closer to the actual grain temperature curve, and the prediction performance of the MCFOA-BP model is better than other models.

Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) is a powerful tool for modeling the inputs and output(s) of complex and nonlinear systems. However, parameters determination for a SVR model is competent to the forecasting accuracy. Several evolutionary algorithms, such as genetic algorithms and simulated annealing algorithms have been used to the parameters selection, however, these algorithms often suffer the problem of being trapped in local optimum. In this paper, a novel building cooling load forecasting approach combining support vector regression (SVR) and Ant colony algorithm (ACO) is proposed. ant colony (ACO) optimization was developed to optimize three parameters of SVR, including penalty parameter C, insensitive loss function ε and kernel function σ. SVR constructs hyperplane in high dimension space and fits the data in non-linear form. Normalized Mean square error (NMSE) of fitting result is used as target of ant colony optimization. ACO finds the best parameters which correspond to the NMSE. The results showed that the proposed approach, by comparing with back-propagation neural network model, was an efficient way to model building cooling load with good predictive accuracy. ACO and SVR provide a useful tool for maximizing the combustion efficiency of cooling load. Also, the method can be easily extended to other applications.

This paper proposes an algorithm for estimating the true boundary values of non-isothermal control heat equations based on binary sampling information from the boundary values, utilizing the Sparrow Search Algorithm (SSA) and Support Vector Regression (SVR) parameter optimization algorithm (hereinafter referred to as SSA-SVR). The performance of the proposed SSA-SVR algorithm was compared with the standalone SVR algorithm and the traditional Particle Swarm Optimization (PSO) algorithm combined with SVR parameter optimization (referred to as PSO-SVR) in estimating boundary values. The results indicate that the proposed SSA-SVR algorithm possesses advantages in estimating boundary values, with faster speed, lower error, and greater stability. This enables more accurate reconstruction of the boundary values, allowing the reconstructed signals to be utilized for other related synchronization controls.

This paper proposes an optimal design scheme to reduce the noise of the intake system by using support vector regression techniques. For this, as a measuring tool for the performance of the intake system, the performance prediction software was used. Then, the length and radius of each component of the current intake system were selected as input variables and the L18 table of orthogonal arrays was adapted as a space-filling design. The simulation of parameter design utilized an orthogonal array design, L 18(21 × 37). In order to evaluate the above design and levels, the experiments satisfying the condition were done. With these simulated data, we can estimate parameters in support vector regression by solving a nonlinear problem and finding an optimal level for the intake system by using support vector regression.

To improve the efficiency of the structural optimization design in truss calculation, an improved fruit fly optimization algorithm was proposed for truss structure optimization. The fruit fly optimization algorithm was a novel swarm intelligence algorithm. In the standard fruit fly optimization algorithm, it is difficult to solve the high-dimensional nonlinear optimization problem and easy to fall into the local optimum. To overcome the shortcomings of the basic fruit fly optimization algorithm, the immune algorithm self–non-self antigen recognition mechanism and the immune system learn–memory–forgetting knowledge processing mechanism were employed. The improved algorithm was introduced to the structural optimization. Optimization results and comparison with other algorithms show that the stability of improved fruit fly optimization algorithm is apparently improved and the efficiency is obviously remarkable. This study provides a more effective solution to structural optimization problems.

Prediction of TVB-N content in eggs based on electronic nose

Forecasting of national expressway network scale is an important part of expressway network planning. It is a complicated problem due to its strong nonlinearity and small quantity of training data. Firstly, An AGA-SVR forecasting model is proposed by combining the support vector regression (SVR) and adaptive genetic algorithm (AGA) in this paper. SVR has been successfully employed to solve regression problems of nonlinearity and problem samples, but selecting appropriate parameters, which is a combinatorial optimization problem, is very crucial to learning performance and generalization performance of SVR. An AGA is used to determine the free parameters of support vector regression. Finally, examples of the scale data of China national expressway network are used to illustrate the performance of the proposed AGA-SVR model. Experimental results demonstrate that the AGA-SVR outperforms the BP neural-network; the AGA-SVR model is a reasonable alternative to forecast national expressway network scale.

The thermal data of the hypersonic wind tunnel field accurately reflect the aerodynamic performance and key parameters of the aircraft model. However, the prediction of the temperature in hypersonic wind tunnels has problems such as a large delay, nonlinearity and multivariable coupling. In order to reduce the influence brought by temperature changes and improve the accuracy of temperature prediction in the field control of hypersonic wind tunnels, this paper first combines kernel principal component analysis (KPCA) with phase space reconstruction to preprocess the temperature data set of wind tunnel tests, and the processed data set is used as the input of the temperature-prediction model. Secondly, support vector regression is applied to the construction of the temperature prediction model for the hypersonic wind-tunnel temperature field. Meanwhile, aiming at the problem of difficult parameter-combination selection in support vector regression machines, an Improved African Vulture Optimization Algorithm (IAVOA) based on adaptive chaotic mapping and local search enhancement is proposed to conduct combination optimization of parameters in support vector regression. The improved African Vulture Optimization Algorithm (AVOA) proposed in this paper was compared and analyzed with the traditional AVOA, PSO (Particle Swarm Optimization Algorithm) and GWO (Grey Wolf Optimizer) algorithms through 10 basic test functions, and the superiority of the improved AVOA algorithm proposed in this paper in optimizing the parameters of the support vector regression machine was verified in the actual temperature data in wind-tunnel field control.

A new hybrid algorithm model for prediction of internal corrosion rate of multiphase pipeline