Interval type-2 fuzzy neural networks for chaotic time series prediction: A concise overview
Chaotic time series widely exists in nature and society (eg, meteorology, physics,
economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent …
economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent …
A hybrid deep learning-based neural network for 24-h ahead wind power forecasting
YY Hong, CLPP Rioflorido - Applied Energy, 2019 - Elsevier
Wind power generation is always associated with uncertainties as a result of fluctuations of
wind speed. Accurate predictions of wind power generation are important for the efficient …
wind speed. Accurate predictions of wind power generation are important for the efficient …
A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series …
M Abdollahzade, A Miranian, H Hassani… - Information …, 2015 - Elsevier
This paper develops a hybrid method for nonlinear and chaotic time series forecasting
based on a local linear neuro-fuzzy model (LLNF) and optimized singular spectrum analysis …
based on a local linear neuro-fuzzy model (LLNF) and optimized singular spectrum analysis …
Short-term load and wind power forecasting using neural network-based prediction intervals
H Quan, D Srinivasan… - IEEE transactions on …, 2013 - ieeexplore.ieee.org
Electrical power systems are evolving from today's centralized bulk systems to more
decentralized systems. Penetrations of renewable energies, such as wind and solar power …
decentralized systems. Penetrations of renewable energies, such as wind and solar power …
A modified interval type-2 Takagi-Sugeno fuzzy neural network and its convergence analysis
In this paper, to compute the firing strength values of type-2 fuzzy models, a soft version of
minimum is presented, which endows the fuzzy model with the ability to solve large …
minimum is presented, which endows the fuzzy model with the ability to solve large …
Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO
In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms
(GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy …
(GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy …
Simplified interval type-2 fuzzy neural networks
YY Lin, SH Liao, JY Chang… - IEEE transactions on …, 2013 - ieeexplore.ieee.org
This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various
applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information …
applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information …
Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction
In this paper a new backpropagation learning method enhanced with type-2 fuzzy logic is
presented. Simulation results and a comparative study among monolithic neural networks …
presented. Simulation results and a comparative study among monolithic neural networks …
Application of interval type-2 fuzzy neural networks in non-linear identification and time series prediction
Abstract Neural networks (NNs), type-1 fuzzy logic systems and interval type-2 fuzzy logic
systems (IT2FLSs) have been shown to be important methods in real world applications …
systems (IT2FLSs) have been shown to be important methods in real world applications …
Multi-time-horizon solar forecasting using recurrent neural network
S Mishra, P Palanisamy - 2018 IEEE energy conversion …, 2018 - ieeexplore.ieee.org
The non-stationarity characteristic of the solar power renders traditional point forecasting
methods to be less useful due to large prediction errors. This results in increased …
methods to be less useful due to large prediction errors. This results in increased …