Add to ORKGThis paper presents a novel dimension of neural networks through the approach of interval systems for great more forecasting activity. The artificial neural network (ANN) based models are the most popular ones for load forecasting and other applications. This approach is only a thought line that can enhance the fundamental requirement of all networks giving and incorporating the analytical and expertise knowledge in forecasting from the existential approaches. [1] Interval systems as an approach to approximate interval models by neural networks is proposed
Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm ...
The problem of output optimization within a specified input space of neural networks (NNs) with fixe...
Interval time series prediction is one of the most challenging research topics in the field of time ...
This paper presents a novel dimension of neural networks through the approach of interval systems fo...
Neural networks can be viewed as nonlinear models, where the weights are parameters to be estimated....
This thesis makes contributions to basic and fundamental research in the field of prediction interva...
Providing solution for short term load forecasting is a\ud major challenge remained for researchers ...
In most industrial systems, forecasts of external demand or predictions of the future system state a...
One of the most important roles in the machine learning area is to classify, and neural networks are...
Abstract: Artificial neural network is a computational intelligence technique that has found major ...
Short-term load forecasting is fundamental for the reliable and efficient operation of power systems...
The paper examines a task of forecasting stock prices of Riga Stock exchange by the use of interval ...
In this paper, a new prediction interval model based on a joint supervision loss function for captur...
summary:Artificial neural networks (ANN) have received a great deal of attention in many fields of e...
Abstract: Neural networks are a consistent example of non-parametric estimation, with powerful unive...
Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm ...
The problem of output optimization within a specified input space of neural networks (NNs) with fixe...
Interval time series prediction is one of the most challenging research topics in the field of time ...
This paper presents a novel dimension of neural networks through the approach of interval systems fo...
Neural networks can be viewed as nonlinear models, where the weights are parameters to be estimated....
This thesis makes contributions to basic and fundamental research in the field of prediction interva...
Providing solution for short term load forecasting is a\ud major challenge remained for researchers ...
In most industrial systems, forecasts of external demand or predictions of the future system state a...
One of the most important roles in the machine learning area is to classify, and neural networks are...
Abstract: Artificial neural network is a computational intelligence technique that has found major ...
Short-term load forecasting is fundamental for the reliable and efficient operation of power systems...
The paper examines a task of forecasting stock prices of Riga Stock exchange by the use of interval ...
In this paper, a new prediction interval model based on a joint supervision loss function for captur...
summary:Artificial neural networks (ANN) have received a great deal of attention in many fields of e...
Abstract: Neural networks are a consistent example of non-parametric estimation, with powerful unive...
Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm ...
The problem of output optimization within a specified input space of neural networks (NNs) with fixe...
Interval time series prediction is one of the most challenging research topics in the field of time ...