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BP neural network (back propagation neural network) is a mathematical model for machine learning. It has a strong advantage in terms of prediction of the future events, and taking into account the different applications, its impact factors are different, which makes the model complex and diverse. A general modeling approach is proposed, which creates and stores BP neural network model dynamically,...
The prestressed loss of group anchor in rock slope increase with time, which leads to the compression belt of structure plane in group anchor area was weakened, deformation of rock surface toward the free surface direction increase gradually, as a result, the slope stability was drastically reduced. Based on the group anchor layout of the abutment rock slope of an arch dam, the anchor-hold monitoring...
Firstly, according to the Beijing urban rail transit network characteristics and based on the data of the historical passenger flow, the passenger flow in sections is distributed and the referenced passenger flow in sections is gotten on the theoretical basis of the shortest path distribution of static unbalanced distribution model. Then through a lot of BP neural network modeling experiments, a reasonable...
High voltage submersible motor works in deep water all the year around, and its operating insulation performance deteriorates influenced by the complex environment. Due to the special installed circumstances, the motor can not be readily maintained. Because of the losses caused by motor deterioration, the prediction of the insulation life-expectancy has a great significance. This paper analyzes the...
The cost of experimental setup during an assembly process development of a chipset, particularly the under-fill process, can often result in insufficient data samples. In INTEL Malaysia, for example, the historical chipset data from an under fill process consists of only a few samples. As a result, existing machine learning algorithms for predictive modeling cannot be applied to this setting. Despite...
Aiming at the disadvantages of prediction model of single BP neural network, a prediction model was presented by combining AdaBoost algorithm and BP neural network for improving the forecasting accuracy of single BP neural network. A new updating method is proposed for the characters of ensemble BP neural network based on AdaBoost. The new method can update the model effectively and overcome the disadvantage...
A forecasting model for gas emission based on wavelet neural network is proposed in this paper. In the model, wavelet neutral network (WNN) is applied to the forecasting with gradient descent and amended by validity of iteration training algorithm. Compared with back-propagation neural networks, forecasting of the model has advantages of faster convergence and more accurate. Simulation results have...
Studies of paleoclimate variations in local regions are seriously restricted by the low resolution and uncertainties of the simulated data at present. In order to apply large-scale modeling data to paleoclimate research in local regions, an effective downscaling model based on three-layer back propagation neural network (BPNN) is developed. Observational and ECHO-G simulated data are employed to train...
Power utilization has become a major issue in portable designs, since its battery storage is less compared to its usage. One of the popular techniques to solve this problem is to use Dynamic Power Management (DPM) at the system level. Dynamic power management is a technique used to save power when the system is idle. Earlier it was assumed that the prediction can be done only in long range dependent...
A GPU-accelerated OpenCL implementation of a back-propagation artificial neural network for the creation of QSAR models for drug discovery and virtual high-throughput screening is presented. A QSAR model for HSD achieved an enrichment of 5.9 and area under the curve of 0.83 on an independent data set which signifies sufficient predictive ability for virtual high-throughput screening efforts. The speed-up...
A new model for predicting the residual value of the private used car with various conditions, such as manufacturer, mileage, time of life, etc., was developed in this paper. A comprehensive method combined by the BP neural network and nonlinear curve fit was introduced for optimizing the model due to its flexible nonlinearity. Firstly, some distribution curves of residual value of the used cars were...
Although many models have been developed for prediction and forecasting of time series in various engineering fields, there is no perfect model to forecast hydrologic time series. In recent decades, Artificial Neural Networks (ANNs) have been very common for prediction and forecasting of hydrologic time series because of their practicality in applications. In this paper, we propose the application...
As the critical demand of market for greater product, how to ensure the minimum inventory of customers in the supply chain and to make a reasonable prediction on the customer needs are particularly essential. The neural network technology will be introduced to VMI customer demand prediction, by means of professional visualization software development, supply chain efficiency improvement and budget...
Groundwater table often shows complex nonlinear characteristic. Back Propagation (BP) neural network is increasingly used to predict groundwater table. But man-made selecting the structure of BP neural network has blindness and expends much time. In order to overcome shortcomings of traditional BP neural network, Particle Swarm Optimization (PSO) algorithm was adopted to automatically search BP neural...
To make an accurate prediction about the amount of equipment maintenance materials consumption (EMMC), which plays an important role of equipment maintenance materials support, precondition and management, an LM algorithm prediction model of EMMC established based on the improved BP neural network algorithm by means of history data processing, and which has been discussed and verified through example...
This paper introduces a new Neural Network model which is suitable for oil production prediction with training parameter set. From the comparison between prediction of oil production and real production, the precision of prediction meets the requirements quite well. In addition, this new model offers better self-adaptive ability and can be used in multi-cycle and multi-descending production forecast...
The time series prediction model based on neural network can perfectly reflect the trend of development of nonlinear system, but the training speed for neural network is very slow, therefore, it is easily prone to local extremum. So we come up with a learning algorithm combining genetic algorithm and BP algorithm for the training of BP neural network, to realize optimization of network structure....
In order to overcome the disadvantages such as low calculation precision and convergence rate of traditional BP neural network algorithm, a kind of nonlinear optimization method-BFGS method for unconstrained extreme problem is introduced into BP neural network algorithm, and a BFGS-BP neural network model is developed, which is applied well in structure deformation monitoring data processing and forecasting...
Gas filow-volume controlled by many factors, the trend is complex, the accurate mathematical model to predict, in view of this situation, the paper attempts to grey dynamic model based on artificial neural network, organic combination of intelligent analysis method, structural gray neural network combination forecast model, based on Visual Basic 6.0, meanwhile, corresponding calculation program is...
For the current problems in the security situation of colliery equipment ,and based on non-linear relationship among the parameters of colliery equipment ,this paper presents a method for forecasting the safety of colliery equipment based on BP neural network.By using BP neural network in the colliery safety equipment monitoring and warning issues,we established a multi-index comprehensive monitoring...
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