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Analyzed the samples of the failure depth of coal seam floor collected in mining fields, studied the main influence factors being associated with the failure depth. In order to avoid overfitting problem of artificial neural network (ANN), a new least squares support vector machines (LS-SVM) model is presented to forecast the nonlinear failure depth of coal seam floor under the influence of mining...
Least squares support vector machines (LS-SVM) method is used for modeling, and its penalty factors and kernel parameters with different values will affect the accuracy of the soft sensor model. This paper presents a particle swarm optimization (PSO) algorithm with mutation to automatically search the parameters for LS-SVM, and is applied to real-time measurement problem of saturated vapor dryness...
Aiming at the characteristics of big inertia, pure lag, the nonlinear in the cement rotary kiln, A new model has been established using the least squares support vector machine intelligent algorithm. Also, the particle swarm optimization algorithm is introduced to carrying out the model analysis in the large amount of data that collected from the cement plant so as to determine optimal parameters...
The problem in parameter selection of least squares support vector machine (LS-SVM) restricts the development of LS-SVM, In order to choose the optimal parameters of LS-SVM automatically, we proposed an improved particle swarm optimization (PSO) algorithm which can not only increase the convergent speed but also improve the overall searching ability of the algorithm. The improved PSO algorithm can...
Intelligent heuristic algorithms have been paid more and more attention in solving large-scale, complex optimization problems. Membrane computing is a new branch of natural computing with the features of distribution and great parallelism. PSO is also a simple and effective intelligent computing method. Considering the features of membrane computing and PSO, a hybrid algorithm MCBPSO is proposed in...
Classification problem is an important and complex problem in machine learning. Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in classification, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques such as least Squares...
In efficiency analysis of weapon system, in order to achieve and represent the preferences of decision maker and make the most optimum selection, an efficiency evaluation method of weapon equipment based on LS-SVM with parameters optimization is proposed. Firstly, the principle of efficiency evaluation method based on LS-SVM is discussed. Secondly, to learn samples from the MADM problem extractly,...
The prediction of chaotic time series is performed by least square support vector machine (LS-SVM) based on particle swarm optimization (PSO). The main objective of this approach is to increase the accuracy of the chaotic time series prediction. For the generation performance of LS-SVM depending on a good setting of its parameters, PSO is adopted to choose the global optimum parameters of LS-SVM automatically...
In order to improve the pressure sensor's current stability and temperature drift performance, a soft sensor regression model was modeled based on least square support vector machine (LS-SVM). According to the difficulty in selecting penalty factor and kernel parameter which are called hyper-parameters in LS-SVM when modeling, particle swarm optimization (PSO) algorithm and ergodicity search algorithm...
The evaluation of competitive power is very important for bidder in power system, how to improve the accuracy and efficiency of evaluation is the keystone people pay attention to, and many researches have been done around it. A combined model of least squares support vector machines optimized by an improved particle swarm optimization algorithm is proposed in this paper to do evaluate the competitive...
Stock return forecast has been an important issue and difficult task for both shareholders and financial professionals. To tackle this problem, we introduce least square support vector machine (LS-SVM), an improved algorithm that regresses faster than standard SVM, and dynamic inertia weight particle swarm optimization (W-PSO), that outperform standard PSO in parameter selection. The work of this...
This paper deals with the study of a water quality prediction model through application of LS-SVM in Liuxi River in Guangzhou. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value, least squares support vector machine (LS-SVM) combined with particle swarm optimization (PSO) is used to time series prediction. The LS-SVM can overcome...
To tackle the problem that the component content is difficult to detect online, an online prediction method of component content in the rare-earth extraction process using soft sensors based on least squares support vector machines (LS-SVM) is proposed. Particle swarm optimization algorithm (PSO) is presented to select the parameters of LS-SVM and the kernel function. The result of simulation indicates...
Considering the shortcomings of conventional cost prediction methods, least squares support vector machine (LS-SVM) was adopted to establish the cost prediction model of equipment system, which could efficiently solve the problems on the determination of network structure and the phenomena of over-fitting in neural network methods. And due to the importance of parameters optimization in LS-SVM model,...
In this paper, a microstrip ferrite circulator is analyzed by FDTD combined with LS-SVM method. Also PSO is used to optimize the hyperparameter used in the LS-SVM algorithm, which should be tried again and again randomly in general. The results compare well with that obtained by the direct FDTD which have a good agreement with the experiment result. This hybrid method can eliminate the late unstability...
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