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 …
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 …
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 …
An interval-valued neural network approach for uncertainty quantification in short-term wind speed prediction
We consider the task of performing prediction with neural networks (NNs) on the basis of
uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty …
uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty …
Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects
Wafer defect detection using an intelligent system is an approach of quality improvement in
semiconductor manufacturing that aims to enhance its process stability, increase production …
semiconductor manufacturing that aims to enhance its process stability, increase production …
Generalized type-2 fuzzy weight adjustment for backpropagation neural networks in time series prediction
In this paper the comparison of a proposed neural network with generalized type-2 fuzzy
weights (NNGT2FW) with respect to the monolithic neural network (NN) and the neural …
weights (NNGT2FW) with respect to the monolithic neural network (NN) and the neural …
Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition
In this paper, a new model of a Modular Neural Network (MNN) optimized with hierarchical
genetic algorithms is proposed. The model uses a granular approach based on the …
genetic algorithms is proposed. The model uses a granular approach based on the …
Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators
This paper describes an optimization of interval type-2 and type-1 fuzzy integrators in
ensembles of ANFIS models with genetic algorithms (GAs), this with emphasis on its …
ensembles of ANFIS models with genetic algorithms (GAs), this with emphasis on its …
Time-series event-based prediction: An unsupervised learning framework based on genetic programming
In this paper, we propose an unsupervised learning framework based on Genetic
Programming (GP) to predict the position of any particular target event (defined by the user) …
Programming (GP) to predict the position of any particular target event (defined by the user) …