Since agriculture is the foundation of a country and the industry that people depend on for life, it is particularly important for the development of national economy, and it has a higher output value than forestry, f...
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Since agriculture is the foundation of a country and the industry that people depend on for life, it is particularly important for the development of national economy, and it has a higher output value than forestry, fishery and animal husbandry, so it occupies a very important position in the economic development of a country. The aim of this paper is to strengthen the capacity of prediction mode for total agricultural output value. This paper provides relevant government departments a reference and solves the problem of the lack of predictive ability of prediction mode for total agricultural output value in previous study. Different from previous literature, this paper adopts the new CFOA to optimize the parameters of GRNN, which contains innovative and reference value in some degree. Besides the way to validate this new model is to take the agricultural output value of the past years as a research sample and test it repeatedly. The study results have indicated that the total agricultural production value accounts for a higher proportion of agriculture, forestry, fishery and animal husbandry and the proportion tends to decline year by year;it can be found through 4 evaluation indexes that the prediction model that optimizes the smoothing parameters of GRNN through CFOA has a better predictive ability than the other two prediction models. (C) 2018 Elsevier B.V. All rights reserved.
Accurate recognition of coal-rock cutting state is a prerequisite for intelligent operation of shearer, so as to achieve safe and efficient production in coal mines. This paper takes the sound signal, Y-axis and Z-axi...
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Accurate recognition of coal-rock cutting state is a prerequisite for intelligent operation of shearer, so as to achieve safe and efficient production in coal mines. This paper takes the sound signal, Y-axis and Z-axis vibration signals as analytic objects and proposes a fusion recognition method for shearer coal-rock cutting state via the combination of improved radical basis function neural network (RBFNN) and Dempster-Shafer (D-S) evidence theory. First of all, on the basis of original fruitflyoptimizationalgorithm (FOA), the location updating mechanism of moth-flame optimization (MFO) is used to improve the convergence performance and exploration ability of FOA. Thus, a hybrid optimizationalgorithm of MFO-FOA is accordingly designed and some simulations are conducted to verify the effectiveness and superiority. Then, the optimal network parameters of RBFNN are found out by using proposed MFO-FOA to realize the excellent generalization ability and predictive performance. Moreover, the collected signals are decomposed by variational mode decomposition, and the envelope entropy and kurtosis are used to extract the features of first three intrinsic mode function components. The feature vectors obtained from three-type sensor data are utilized to construct the RBFNN classifiers. Besides, the D-S evidence theory with evidence correlation coefficient is introduced to fuse the preliminary identification results of three RBFNN classifiers. Finally, a self-designed experimental platform for shearer cutting coal-rock is built and some experiments are provided. The experimental results based on measured data demonstrate that the proposed method can effectively identify the coal-rock cutting state with higher accuracy.
To improve the modeling accuracy and efficiency of the tool wear monitoring system, a generalized regression neural network is adopted to build the tool wear prediction model because its excellent performance on learn...
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To improve the modeling accuracy and efficiency of the tool wear monitoring system, a generalized regression neural network is adopted to build the tool wear prediction model because its excellent performance on learning speed and fast convergence to the optimal results whether the sample data are small or large. The low predictive accuracy and efficiency are caused by traditionally manual adjustment of the spread parameters in generalized regression neural network and then the improved fruit fly optimization algorithm is proposed to optimize the spread parameters of regression neural network automatically. Combining the improvedfruitflyoptimization and generalized regression neural network, the tool wear prediction method is proposed in the paper. Various experiments are carried out to validate the proposed method and the comparison results show a good agreement. In addition, the proposed method is compared to the tool wear prediction method in the literature, and the comparison results also show that the proposed method can achieve better performance.
Aiming at the problem of low reliability of electric power equipment failure rate analyzed by using the data of statistical equipment, this paper puts forward health index, which is according to the actual operation s...
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ISBN:
(纸本)9781538621653
Aiming at the problem of low reliability of electric power equipment failure rate analyzed by using the data of statistical equipment, this paper puts forward health index, which is according to the actual operation status, the evaluation and maintenance of the equipment, to reflect the operation condition of the equipment, and to calculate the failure rate of the equipment. It adopts improved Drosophila optimizationalgorithm to calculate the failure rate function of the equipment. The influence of the different maintenance methods and the change of the equipment performance on the failure rate have also be considered. By introducing age reduction factor, failure rate decline factor and the influence on the failure rate of the equipment after the maintenance, the equivalent service age of the equipment after the maintenance is determined, and the failure rate of the electrical equipment in the background of the state maintenance is realized prediction. Finally, after the analysis of the actual example, the results show that the algorithm and the prediction model have the validity and high efficiency.
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