A review on computational intelligence for identification of nonlinear dynamical systems
This work aims to provide a broad overview of computational techniques belonging to the
area of artificial intelligence tailored for identification of nonlinear dynamical systems. Both …
area of artificial intelligence tailored for identification of nonlinear dynamical systems. Both …
Using radial basis function networks for function approximation and classification
Y Wu, H Wang, B Zhang, KL Du - … Scholarly Research Notices, 2012 - Wiley Online Library
The radial basis function (RBF) network has its foundation in the conventional approximation
theory. It has the capability of universal approximation. The RBF network is a popular …
theory. It has the capability of universal approximation. The RBF network is a popular …
Extreme learning machines: a survey
GB Huang, DH Wang, Y Lan - … journal of machine learning and cybernetics, 2011 - Springer
Computational intelligence techniques have been used in wide applications. Out of
numerous computational intelligence techniques, neural networks and support vector …
numerous computational intelligence techniques, neural networks and support vector …
PSO-based analysis of Echo State Network parameters for time series forecasting
Abstract Echo State Networks, ESNs, are standardly composed of additive units undergoing
sigmoid function activation. They consist of a randomly recurrent neuronal infra-structure …
sigmoid function activation. They consist of a randomly recurrent neuronal infra-structure …
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
GB Huang, P Saratchandran… - IEEE transactions on …, 2005 - ieeexplore.ieee.org
This work presents a new sequential learning algorithm for radial basis function (RBF)
networks referred to as generalized growing and pruning algorithm for RBF (GGAP-RBF) …
networks referred to as generalized growing and pruning algorithm for RBF (GGAP-RBF) …
Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm
J Wu, J Long, M Liu - Neurocomputing, 2015 - Elsevier
In this paper, an effective hybrid optimization strategy by incorporating the adaptive
optimization of particle swarm optimization (PSO) into genetic algorithm (GA), namely …
optimization of particle swarm optimization (PSO) into genetic algorithm (GA), namely …
An investigation of complex fuzzy sets for large-scale learning
Complex fuzzy sets are an extension of type-1 fuzzy sets with complex-valued membership
functions. Over the last 20 years, time-series forecasting has emerged as the most important …
functions. Over the last 20 years, time-series forecasting has emerged as the most important …
Chaotic time series prediction with residual analysis method using hybrid Elman–NARX neural networks
M Ardalani-Farsa, S Zolfaghari - Neurocomputing, 2010 - Elsevier
Residual analysis using hybrid Elman–NARX neural network along with embedding
theorem is used to analyze and predict chaotic time series. Using embedding theorem, the …
theorem is used to analyze and predict chaotic time series. Using embedding theorem, the …
Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction
Cooperative coevolution decomposes a problem into subcomponents and employs
evolutionary algorithms for solving them. Cooperative coevolution has been effective for …
evolutionary algorithms for solving them. Cooperative coevolution has been effective for …