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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...
The aim of this research is to develop an intelligent automated online forecasting of a car fuel consumption using neural network and classified it into classes of driving style. A new online monitoring tool was developed to acquire and analyze data collected from a car for the purpose of fuel consumption modelling and forecasting. The data was transmitted via ECU Can Bus attach to the car to the...
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...
This paper discusses on the adaptive neural network model for predicting the energy consumption at a metering station. The function of the metering system is to calculate the energy consumption of the outgoing gas flow. To ensure the robustness of the developed model, it is suggested to make the model an adaptive model that will periodically update the weights. This will ensure the reliability of...
Artificial Neural networks ANNs are dynamic systems which have the ability not only to capture the relationship between input and output parameters of complex systems but also highly effective when there is no any mathematical formula or model for the system. Therefore, they are very potential and appropriate for design of systems whose functions cannot be expressed explicitly in the form of mathematical...
Recent evidence in neuroscience support the theory that prediction of spatial and temporal patterns in the brain plays a key role in human actions and perception. Inspired by these findings, a system that discriminates laughter from speech by modeling the spatial and temporal relationship between audio and visual features is presented. The underlying assumption is that this relationship is different...
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...
Coverage model is the main technique to evaluate the thoroughness of dynamic verification of a Design-under-Verification (DUV). However, rather than achieving a high coverage, the essential purpose of verification is to expose as many bugs as possible. In this paper, we propose a novel verification methodology that leverages the early bug prediction of a DUV to guide and assess related verification...
As an integral part of reliable communication in wireless networks, effective link estimation is essential for routing protocols. However, due to the dynamic nature of wireless channels, accurate link quality estimation remains a challenging task. In this paper, we propose 4C, a novel link estimator that applies link quality prediction along with link estimation. Our approach is data-driven and consists...
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...
Physical activity (PA) can influence heart rate(HR). But the relationship between HR and PA is hard to describe. In our previous works, HR prediction models based on PA were designed. However, the prediction time length and accuracy are usually hard to compromise. In this study, a new HR prediction method is proposed. The predicted HR is used as the input in the next prediction step. Only HR at the...
Researchers have known for some time that non-linearity exists in the financial markets and that neural networks can be used to forecast market returns. In this article, we present a novel stock market prediction system which focuses on forecasting the relative tendency growth between different stocks and indices rather than purely predicting their values. This research utilizes artificial neural...
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