In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of ga-B...
详细信息
In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of ga-bp hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （bp-ANN） and genetic algorithm （ga）. The detailed process was as follows. Firstly, the ga trained the best weights and thresholds as the initial values of bp-ANN to initialize the neural network. Then, the bp-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the ga-bp network model was trained until getting the desired network error only by 46 steps, while the single bp-ANN model achieved the same network error by 992 steps, which obviously shows that the ga-bp hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by ga-bp hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by bp-ANN is 2.950%, which clearly indicates that the forecast precision of the ga-bp hybrid algorithm is higher than that of the bp-ANN.
Based on the extensive operations of polygonal fuzzy numbers, a ga-bp hybrid algorithm for polygonal fuzzy neural network is designed. Firstly, an optimal solution is obtained by the global searching ability of ga alg...
详细信息
Based on the extensive operations of polygonal fuzzy numbers, a ga-bp hybrid algorithm for polygonal fuzzy neural network is designed. Firstly, an optimal solution is obtained by the global searching ability of gaalgorithm for the untrained polygonal fuzzy neural network. Secondly, some parameters for connection weights and threshold values are appropriately optimized by using an improved bpalgorithm. Finally, through a simulation example, we demonstrate that the ga-bp hybrid algorithm based on the polygonal fuzzy neural network can not only avoid the initial values' dependence and local convergence of the original bpalgorithm, but also overcome a blindness problem of the traditional gaalgorithm.
Due to its large axle load and high-density operation mode, heavy haul transportation has greatly improved the cargo transportation capacity, and is receiving unprecedented attention from all countries in the world. S...
详细信息
Due to its large axle load and high-density operation mode, heavy haul transportation has greatly improved the cargo transportation capacity, and is receiving unprecedented attention from all countries in the world. Since the development of heavy haul freight transport in China, wheel rail wear has been paid much attention, especially the use of heavy axle load locomotives on upgraded heavy haul lines, which makes reducing wheel rail wear and damage become a technical problem to be solved urgently. Considering that there are too many mechanical parameters involved in the prediction of heavy load wheel rail wear mechanical properties, the prediction accuracy is reduced. Therefore, this paper proposes a method based on ga-bp hybrid algorithm to predict the mechanical properties of heavy load wheel/rail wear. Hertz contact theory is used to simplify the wheel rail contact relationship, and the wheel rail contact model is established. According to the wheel/rail contact model, the expressions of heavy load wheel/rail in the case of vertical, horizontal, direction and gauge irregularity are analyzed, and based on this, a mechanical model of heavy load wheel/rail wear is established. In order to solve the problems of slow convergence speed and easy to fall into local optimum of bp neural network in the prediction of heavy load wheel/rail wear mechanical properties, the global convergence of genetic algorithm is used to optimize the bp network. According to the obtained mechanical parameters of heavy load wheel/rail wear, the mechanical parameters are input into the optimized model, and the relevant prediction results are output. So far, the research on the prediction method of heavy load wheel/rail wear mechanical properties based on ga-bp hybrid algorithm has been realized. The experiment is designed from three aspects of wear degree, hardness and tensile strength, and compared with the measured value, reference [4] method, reference [5] method and reference [6] method t
Safety monitoring and stability analysis of high slopes are important for high dam construction in mountainous regions or precipitous gorges. Slope stability estimation is an engineering problem that involves several ...
详细信息
Safety monitoring and stability analysis of high slopes are important for high dam construction in mountainous regions or precipitous gorges. Slope stability estimation is an engineering problem that involves several parameters. To address these problems, a hybrid model based on the combination of Genetic algorithm (ga) and Back-propagation Artificial Neural Network (bp-ANN) is proposed in this study to improve the forecasting performance. ga was employed in selecting the best bp-ANN parameters to enhance the forecasting accuracy. Several important parameters, including the slope geological conditions, location of instruments, space and time conditions before and after measuring, were used as the input parameters, while the slope displacement was the output parameter. The results shown that the ga-bp model is a powerful computational tool that can be used to predict the slope stability.
暂无评论