نتایج جستجو برای: regression modelling bayesian regularization neural network
تعداد نتایج: 1338314 فیلتر نتایج به سال:
Abstra t We demonstrate the advantages of using Bayesian neural networks in regression, inverse and lassi ation problems, whi h are ommon in industrial appli ations. The Bayesian approa h provides onsistent way to do inferen e by ombining the eviden e from data to prior knowledge from the problem. A pra ti al problem with neural networks is to sele t the orre t omplexity for the model, i.e., th...
Estimation (Forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. Thus, accuracy of the estimation is highly desirable. Hibrid Regression Neural Network is an approach proposed in this paper to obtain better fitness in comparison with Regression Analysis and the Neural Network methods. Comparing the estimated resul...
Flood is a kind of natural disaster which causes financial damages and fatality for people. Every year, especially in areas like Maroon river basin which have changes in precipitation and temperatures, along with frequent and severe floods. This study aimed to identify the climatic parameters on flood area can be efficiently artificial neural network, better methods applied in anticipation of t...
In order to avoid over tting in neural learning, a regularization term is added to the loss function to be minimized. It is naturally derived from the Bayesian standpoint. The present paper studies how to determine the regularization constant from the points of view of the empirical Bayes approach, the maximum description length (MDL) approach, and the network information criterion (NIC) approa...
Selecting appropriate inputs for intelligent models is important due to reduce costs and save time and increase accuracy and efficiency of models. The purpose of this study is using Shannon entropy to select the optimum combination of input variables in time series modeling. Monthly time series of precipitation, temperature and radiation in the period of 1982-2010 was used from Tabriz synoptic ...
background: data mining (dm) is an approach used in extracting valuable information from environmental processes. this research depicts a dm approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (wsp) in birjand, a city in eastern iran. methods: multiple regression (mr) and neural network (nn) models were examined us...
The future of development and design is impossible without study of Power Flow(PF), exigency the system outcomes load growth, necessity add generators, transformers and power lines in power system. The urgency for Optimal Power Flow (OPF) studies, in addition to the items listed for the PF and in order to achieve the objective functions. In this paper has been used cost of generator fuel, acti...
One of the most important hopes in modern finance is finding the most accurate ways to forecast future values of exchange rates.The research provides some evidence about the efficiency of utilizing neural network models in forecasting foreign exchange rates. We used three distinct learning algorithms in our neural network models, namely,Scaled Conjugate Gradient (SCG), Standard Backpropagation ...
This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...
نمودار تعداد نتایج جستجو در هر سال
با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید