ARFNNs with SVR for prediction of chaotic time series with outliers

Fu, Yangyang; Wu, Chia‐Ju; Jeng, Jin-Tsong; Ko, Chia-Nan

doi:10.1016/j.eswa.2009.12.067

Cited by 26 publications

(13 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and ( 18) x t − are the inputs of the proposed FNNs; ( 6) x t + is the output of the FNNs. In the simulation, 1000 time-delay series are generated from (27) with initial value (0) 1.2.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Example: A typical chaotic system, Mackey Glass time-delay series, is considered as an example described as follows [6]: [6,20], the time series are chosen as ( 6) ( ( ), ( 6), ( 12), ( 18 )),…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Due to their characteristics sensitivity to initial conditions, chaotic systems are not easy to identify. Recently, some researchers have endeavored to improve the identification of chaotic time series [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of time-delay chaotic system with outliers: Fuzzy neural networks using hybrid learning algorithm

Liu

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

Self Cite

View full text Add to dashboard Cite

A hybrid learning algorithm is proposed to train fuzzy neural networks (FNNs) for identifying a time-delay chaotic system with outliers. In the proposed algorithm, integrating support vector regression (SVR) and annealing robust timevarying learning algorithm (ARTVLA) to optimize FNNs. In the evolutionary procedure, first, SVR is adopted to determine the number of hidden layer nodes and the initial structure of the FNNs. After initialization, ARTVLA with nonlinear time-varying learning rate is then applied to train FNNs. In ARTVLA, the determination of the learning rate would be an important work for the trade-off between stability and speed of convergence. A computationally efficient optimization method, particle swarm optimization (PSO) method, is adopted to simultaneously find optimal learning rates. Due to the advantages of SVR and ARTVLA (SVR-ARTVLA), the proposed FNNs (SVR-ARTVLAFNNs) have good performance for identifying a time-delay chaotic system: Mackey Glass system with outliers. Simulation results are illustrated the effectiveness and feasibility of the proposed SVR-ARTVLA-FNNs.

show abstract

“…and ( 18) x t − are the inputs of the proposed FNNs; ( 6) x t + is the output of the FNNs. In the simulation, 1000 time-delay series are generated from (27) with initial value (0) 1.2.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Identification of time-delay chaotic system with outliers: Fuzzy neural networks using hybrid learning algorithm

Liu

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

Self Cite

View full text Add to dashboard Cite

show abstract

“…[9,10] The first group comprises various neural networks models which able to reveal and approximate local tendencies in observed data. [11,12] The second one includes fuzzy and neuro-fuzzy approaches employed to construct robust and logically transparent models of identification. [13] The third group is associated with distributed artificial intelligence, namely, with genetic algorithms, [14] with swarm intelligence, [15] with ant colony optimization [16] and with other algorithm belonging to this group.…”

Section: Related Workmentioning

confidence: 99%

Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations

Gromov

Voronin

Gatylo

et al. 2017

AIR

View full text Add to dashboard Cite

The paper deals with a novel algorithm used to improve identification quality of clusters generated by predictive clustering algorithm as a tool to identify states preceding to bifurcations for a system governed by von Karman equations. To construct bifurcation precursors, solutions (of the equations) observed on bifurcation paths are clustered; centers of the clusters constitute a set of bifurcation precursors. To decrease identification error rate, quality of each precursor is assessed with the employment of an additional, validation set. The paper concerns with two approaches to this procedure; the first one employs a single number to assess identification value of a cluster in order to delete those with low identification values. The second approach uses proposed knowledge extraction procedure to ascertain rules of replacement of the precursors chosen by the algorithm (active) by more efficient one. A wide-ranging simulation reveals that the best variant (provided that the Wishart clustering algorithm is utilized) is the replacement of the active cluster in conjunction local normalization of data. The optimal parameters values for both algorithms, arriving at essentially decreased identification errors.

show abstract

“…One may find hybrids of fuzzy and Radial Basis Function networks trained with Particle Swarm Optimization (PSO) [24], Support Vector Machines (SVM), RBF and fuzzy inference [25], Support Vector Regression and RBF networks [21].…”

Section: Literature Review and Contributions Of The Papermentioning

confidence: 99%

Variational inference for robust sequential learning of multilayered perceptron neural network

Vuković¹,

Mitić²,

Miljković³

2015

FME Transaction

View full text Add to dashboard Cite

ARFNNs with SVR for prediction of chaotic time series with outliers

Cited by 26 publications

References 9 publications

Identification of time-delay chaotic system with outliers: Fuzzy neural networks using hybrid learning algorithm

Identification of time-delay chaotic system with outliers: Fuzzy neural networks using hybrid learning algorithm

Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations

Variational inference for robust sequential learning of multilayered perceptron neural network

Contact Info

Product

Resources

About