Backtracking search optimization algorithm-based least square support vector machine and its applications

Developing the models of multiple processes rapidly and accurately is very important for process control and optimization in industrial production. This paper proposes a multiprocess joint modeling method to establish the models of multiple industrial processes simultaneously. Under the assumption that the related processes shared common information, this method views the process model as the combination of the common feature model and the special feature model. Through the full mining and using the shared information, the proposed method can establish models of multiple processes simultaneously and improve the accuracy of each process model. In particular, an improved multitask least squares support vector machine algorithm, which assumes that all the learning processes share the common feature model, is proposed to train multiprocess models simultaneously. Experiments developing the models of multiple centrifugal compressors are conducted, and the simulation experiment results show that the proposed method can realize joint modeling of multiple similar processes, improve modeling efficiency, and reduce modeling costs. 

A Multiprocess Joint Modeling Method for Performance Prediction of Nonlinear Industrial Processes Based on Multitask Least Squares Support Vector Machine

Joint sparse representation (JSR) is a commonly used classifier that recognizes different objects with core features extracted from images. However, the generalization ability is weak for the traditional linear kernel, and the objects with similar feature values associated with different categories are not sufficiently distinguished especially for a hyperspectral image (HSI). In this article, an HSI classification technique based on the weight wavelet kernel JSR ensemble model and the $\beta$ -whale optimization algorithm is proposed to conduct pixel-level classification, where the wavelet function is acted as the kernel of JSR. Moreover, ensemble learning is used to determine the category label of each sample by comprehensive decision of some subclassifiers, and the $\beta$ function is utilized to enhance the exploration phase of the whale optimization algorithm and obtain the optimal weight of subclassifiers. Experimental results indicate that the performance of the proposed HSI classification method is better than that of other newly proposed and corresponding approaches, the misclassification and classified noise are eliminated to some extent, and the overall classification accuracy reaches 95% for all HSIs.

/pdf/a-hyperspectral-image-classification-method-based-on-weight-3yg7me9i4u.pdf

A Hyperspectral Image Classification Method Based on Weight Wavelet Kernel Joint Sparse Representation Ensemble and β-Whale Optimization Algorithm

. Aiming at the problem that the fitting parameters of the least squares support vector machine fitting method are difficult to select, a method of introducing the artificial bee colony algorithm into the least squares support vector machine to establish a high-precision region fitting model is proposed. The artificial bee colony algorithm can perform global tracking search on the parameters in the least squares support vector machine, imitate the honey collecting process of the bees, and use the primary value of the parameters as the honey source, and the average square error predicted by the least squares support vector machine as the target. The function value is determined by iterative update within a certain range to determine the optimal parameters, and finally a GPS height fitting model with higher precision is established. Experimental analysis, compared with the conventional least squares support vector machine fitting method, the accuracy of the fitting model constructed by the ABC-LSSVM combination method is improved by 45.4%. At the same time, the combined method is better than the particle swarm optimization fitting method and BP neural network. The legal convergence effect is higher and the stability is better. The effective feasibility of the ABC-LSSVM combination method in the construction of GPS height fitting model is proved, which provides a certain reference value for the establishment of GPS height fitting model.

/pdf/research-on-gps-height-fitting-based-on-abc-optimized-lssvm-134010fjjm.pdf

Research on gps height fitting based on abc optimized lssvm

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China’s regional carbon price.

/pdf/prediction-of-regional-carbon-price-in-china-based-on-5tqbm7to.pdf

Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction

Wavelet kernel function based multiscale LSSVM for elliptic boundary value problems

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Wavelet kernel function based multiscale LSSVM for elliptic boundary value problems