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Neuro-fuzzy prediction of the glucose level of patients with insulin-dependent diabetes
Engineering Education # 11, November 2010
Optimal doses of artificial insulin depend on a variety of factors. Adjustment of insulin dosages is a sophisticated task and can be far too complex for most patients. In order to simplify the solution to the problem the Continuous Glucose Monitoring System (CGMS) and continuous subcutaneous insulin infusion systems (insulin pumps) were developed.Blood glucose level (BG) control systems based on CGMS and insulin pumps are being actively developed. Algorithmically, these systems consist of two subsystems: the BG forecasting subsystem and the optimal dose determination subsystem. The problem of blood glucose forecasting in type I diabetes patients is being researched here. Artificial neural network (ANN)-based and maximum similarity extrapolation model based on maximum likeness set (EMMLS)-based approaches have been considered. The approach effectiveness has been compared. It is shown here, that neural networks provide more accurate results for short-term forecasting, while EMMLS is more accurate on long-term forecasting. Prospects of combined EMMLS and ANN model are also shown.
Maximum likeness extrapolation model for REM (Russian energy market) time series
Engineering Education # 01, January 2010
In article is reviewed base of maximum likeness extrapolation first. Then there are a few recommendations for model identification based on researched time series. Later there are results of extrapolation of researched time series. In the end there is mention of model features and further development of maximum likeness extrapolation model. All researched was done on REM time series (prices and volumes).
Extrapolation of pseudorandom number sequence on maximum likeness
Engineering Education # 07, July 2009
This research in the field of extrapolation has begun in 2008 from developing model for extrapolation day ahead price on Russian energy market. Lately we notice that general idea of method is new so we decided to expand it for other pseudo random sequences. Objective of research is to obtain general algorithm of extrapolation of different pseudo random process (market and physical). We would like to implement comparison to any other models so we are open for any kind of cooperation
 
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