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The unified Parkinson's disease rating scale (UPDRS) is the most widely employed scale for tracking Parkinson's disease (PD) symptom progression. However, conventional way to achieve UPDRS, mainly based on the physical examinations of clinic patients performed by the trained medical staffs, involves the disadvantages of inconvenience and high medical expense. Hence, in this study, we try to explore...
In recent years, type II diabetes has become a serious disease that threaten the health and mind of human. Efficient predictive modeling is required for medical researchers and practitioners. This study proposes a type II diabetes prediction model based on random forest which aims at analyzing some readily available indicators (age, weight, waist, hip, etc.) effects on diabetes and discovering some...
Crime Hot Spots refer to the areas in which the crime rates are above the average level, therefore the Hot Spots Prediction is the primary mission of the Public Security Prevention and Control. By encoding the area-specific crime incidents, the crime hot spots has been classified them into different heat levels, rendering the conversion of Hot Spots prediction into a multi-class classification problem...
In order to use the method based on model to study the accurate control of cement combined grinding system, and improve its automatic control level, this paper proposes a extreme learning machine (ELM) online modeling method for combined cement grinding system. First of all, this paper analyzes the process of combined grinding system, based on the analysis of the process, we know that the speed of...
In this paper, a forecasting-mean-correlation-entropy portfolio optimization model (FMCE) is developed by using the fuzzy time series techniques to predict securities' future returns distribution and employing entropy as risk measurement. Traditional portfolio models such as MV model have stringent conditions to returns distribution, while entropy as a new risk measurement is free from these restrictions...
Firstly, an air passenger capacity investigation at the capital international airport is made, and a composite forecasting model based on total air passenger capacity is established, in which multiple regression and ARIMA model are parallel connection and their forecast results are series connection with BP neural network. Secondly, according to the average growth rate of air passenger capacity, all...
This paper provides a forecast method based on Gray Residual-Back Propagation neural network (GRPBNN), which predicts automobile logistics demand and accomplishes it by using matlab workbox. The forecast result of the logistics demand of a certain car shows that it matches the real figure. The result forecasted by this method is accurate, and the fitting accuracy is acceptable.
In order to study and improve the emission performance of WCS/diesel DFE, an emission model for DFE based on radial basis function neural network was developed which was a black-box input-output training data model not require priori knowledge .Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. And...
Considering the difficulty of large time-delay process control, take advantage of the approaching ability of neural network to nonlinear system, a smith predictor with neural network is constructed. At the same time, use identification-free adaptive algorithm to adjust the coefficient of the single-neuron PSD controller. Combine them, a new control method appear. The result of the simulation indicated...
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