Bayesian regularized NAR neural network based short-term prediction method of water consumption
نویسندگان
چکیده
منابع مشابه
Short term electric load prediction based on deep neural network and wavelet transform and input selection
Electricity demand forecasting is one of the most important factors in the planning, design, and operation of competitive electrical systems. However, most of the load forecasting methods are not accurate. Therefore, in order to increase the accuracy of the short-term electrical load forecast, this paper proposes a hybrid method for predicting electric load based on a deep neural network with a...
متن کاملShort-term prediction of atmospheric concentrations of ground-level ozone in Karaj using artificial neural network
Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level...
متن کاملShort-term prediction of atmospheric concentrations of ground-level ozone in Karaj using artificial neural network
Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level...
متن کاملShort-term Prediction of Tehran Stock Exchange Price Index (TEPIX): Using Artificial Neural Network (ANN)
The main objective of this study is to find out whether an Artificial Neural Network (ANN) will be useful to predict stock market price, which is highly non-linear and uncertain. Specifically, this study will focus on forecasting TSE Price Index (TEPIX) as the most significant index of Iran Stock Market. Many data have been used as inputs to the network. These data are observations of 2000 day...
متن کاملA New Approach for Investigating the Complexity of Short Term EEG Signal Based on Neural Network
Background and purpose: The nonlinear quality of electroencephalography (EEG), like other irregular signals, can be quantified. Some of these values, such as Lyapunovchr('39')s representative, study the signal path divergence and some quantifiers need to reconstruct the signal path but some do not. However, all of these quantifiers require a long signal to quantify the signal complexity. Mate...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: E3S Web of Conferences
سال: 2019
ISSN: 2267-1242
DOI: 10.1051/e3sconf/201911803024