Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 10/2021

31-08-2020 | Original Article

A self-organizing recurrent fuzzy neural network based on multivariate time series analysis

Authors: Haixu Ding, Wenjing Li, Junfei Qiao

Published in: Neural Computing and Applications | Issue 10/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Fuzzy neural networks (FNNs) have attracted considerable interest for modeling nonlinear dynamic systems in recent years. However, the recurrent design and the self-organizing design of FNNs generally lack adaptability, and their analyses on the change rule of networks in continuous time are insufficient. To solve these problems, a self-organizing recurrent fuzzy neural network based on multivariate time series analysis (SORFNN-MTSA) is proposed in this paper. First, a recurrent mechanism, based on wavelet transform fuzzy Markov chain algorithm, is introduced to obtain adaptive recurrent values and accelerate convergence speed of the network. Second, a self-organization mechanism, based on weighted dynamic time warping algorithm and sensitivity analysis algorithm, is presented to optimize the network structure. Third, the convergence of SORFNN-MTSA is theoretically analyzed to show the efficiency in both fixed structure and self-organizing structure cases. Finally, several benchmark nonlinear systems and a real application of wastewater treatment are used to verify the effectiveness of SORFNN-MTSA. Compared with other existing methods, the proposed SORFNN-MTSA performs better in terms of both high accuracy and compact structure.
previous article Skills prediction based on multi-label resume classification using CNN with model predictions explanation
next article Execution examination of chaotic S-box dependent on improved PSO algorithm

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Shihabudheen KV, Pillai GN (2018) Recent advances in neuro-fuzzy system: a survey. Knowledge-Based Syst 152:136–162
2.
Almási AD, Woźniak S, Cristea V et al (2016) Review of advances in neural networks: neural design technology stack. Neurocomputing 174:31–41
3.
Ebadzadeh MM, Salimi-Badr A (2018) IC-FNN: a novel fuzzy neural network with interpretable, intuitive, and correlated-contours fuzzy rules for function approximation. IEEE Trans Fuzzy Syst 26(3):1288–1302
4.
5.
6.
Wu GD, Zhu ZW (2014) An enhanced discriminability recurrent fuzzy neural network for temporal classification problems. Fuzzy Sets Syst 237:47–62MathSciNetMATH
7.
8.
Luo C, Tan C, Wang X, Zheng Y (2019) An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction. Appl Soft Comput J 78:150–163
9.
Bencherif A, Chouireb F (2019) A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking. Appl Intell 49:3881–3893
10.
Premkumar K, Manikandan BV, Kumar CA (2017) Antlion algorithm optimized fuzzy PID supervised on-line recurrent fuzzy neural network based controller for brushless DC motor. Electr Power Compon Syst 45:2304–2317
11.
Wen Z, Xie L, Fan Q, Feng H (2020) Long term electric load forecasting based on TS-type recurrent fuzzy neural network model. Electr Power Syst Res 179:106106
12.
Qiao JF, Han GT, Han HG et al (2019) Decoupling control for wastewater treatment process based on recurrent fuzzy neural network. Asian J Control 21:1270–1280MathSciNetMATH
13.
Chen X, Xue AK, Peng DL, Guo Y (2014) Modeling of pH neutralization process using fuzzy recurrent neural network and DNA based NSGA-II. J Franklin Inst 351:3847–3864MATH
14.
15.
Mastorocostas PA, Hilas CS, Varsamis DN, Dova SC (2016) Telecommunications call volume forecasting with a block-diagonal recurrent fuzzy neural network. Telecommun Syst 63:15–25
16.
Vineetha S, Chandra Shekara Bhat C, Idicula SM (2012) Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network. Gene 506:408–416
17.
Qiao JF, Han GT, Han HG, Chai W (2017) Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network. Int J Intell Comput Cybern 10:94–110
18.
Uyar K, Ilhan A (2017) Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. Procedia Comput Sci 120:588–593
19.
Lu CH, Wang WC, Tai CC, Chen TC (2016) Design of a heart rate controller for treadmill exercise using a recurrent fuzzy neural network. Comput Methods Programs Biomed 128:27–39
20.
Chen CS (2010) TSK-type self-organizing recurrent-neural-fuzzy control of linear microstepping motor drives. IEEE Trans Power Electron 25:2253–2265
21.
Qiao JF, Cai J, Han HG, Cai JX (2017) Predicting PM2.5 concentrations at a regional background station using second order self-organizing fuzzy neural network. Atmosphere (Basel) 8:1–17
22.
Zhao TY, Li P, Cao JT (2019) Soft sensor modeling of chemical process based on self-organizing recurrent interval type-2 fuzzy neural network. ISA Trans 84:237–246
23.
Han HG, Zhang L, Wu XX, Qiao JF (2019) An efficient second-order algorithm for self-organizing fuzzy neural networks. IEEE Trans Cybern 49:14–26
24.
Sabahi F (2018) Introducing validity into self-organizing fuzzy neural network applied to impedance force control. Fuzzy Sets Syst 337:113–127MathSciNetMATH
25.
Wen ZT, Xie LB, Feng HW, Tan Y (2019) Infrared flame detection based on a self-organizing TS-type fuzzy neural network. Neurocomputing 337:67–79
26.
Han HG, Lin ZL, Qiao JF (2017) Modeling of nonlinear systems using the self-organizing fuzzy neural network with adaptive gradient algorithm. Neurocomputing 266:566–578
27.
Juang CF, Da HC (2010) A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling. IEEE Trans Fuzzy Syst 18:261–273
28.
Wai RJ, Lin YW (2012) Adaptive moving-target tracking control of a vision-based mobile robot via a dynamic petri recurrent fuzzy neural network. IEEE Trans Fuzzy Syst 21(4):688–701
29.
Lin FJ, Shyu KK, Wai RJ (2001) Recurrent-fuzzy-neural-network sliding-mode controlled motor-toggle servomechanism. IEEE/ASME Trans Mechatron 6(4):453–466
30.
Han SI, Lee JM (2014) Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems. ISA Trans 53:33–43
31.
Xu GZ, Song AG, Li HJ (2011) Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural network. J Intell Robot Syst 62(3–4):501–525MATH
32.
Han HG, Chen ZY, Liu HX, Qiao JF (2018) A self-organizing interval Type-2 fuzzy-neural-network for modeling nonlinear systems. Neurocomputing 290:196–207
33.
Mohammadzadeh A, Ghaemi S (2016) A modified sliding mode approach for synchronization of fractional-order chaotic/hyperchaotic systems by using new self-structuring hierarchical type-2 fuzzy neural network. Neurocomputing 191:200–213
34.
Wang C, Agarwal RP, O’Regan D (2019) Calculus of fuzzy vector-valued functions and almost periodic fuzzy vector-valued functions on time scales. Fuzzy Sets Syst 375:1–52MathSciNetMATH
35.
Wang C (2014) Almost periodic solutions of impulsive BAM neural networks with variable delays on time scales. Commun Nonlinear Sci Numer Simul 19(8):2828–2842MathSciNetMATH
36.
Sakthivel R, Wang C, Santra S, Kaviarasan B (2018) Non-fragile reliable sampled-data controller for nonlinear switched time-varying systems. Nonlinear Anal Hybrid Syst 27:62–76MathSciNetMATH
37.
Kam HJ, Sung JO, Park RW (2010) Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthc Inform Res 16(3):158–165
38.
Joo TW, Kim SB (2015) Time series forecasting based on wavelet filtering. Expert Syst Appl 42(8):3868–3874
39.
Sun W, Xu YF (2017) Research on China’s energy supply and demand using an improved Grey–Markov chain model based on wavelet transform. Energy 118:969–984
40.
Lin CJ, Lee CY (2010) Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation. Int J Syst Sci 41(4):381–395MATH
41.
Lauret P, Fock E, Mara TA (2006) A node pruning algorithm based on a Fourier amplitude sensitivity test method. IEEE Trans Neural Netw 17(2):273–293
42.
Vuković N, Miljković Z (2013) A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation. Neural Netw 46:210–226MATH
43.
Han HG, Qiao JF (2013) A structure optimisation algorithm for feedforward neural network construction. Neurocomputing 99:347–357
44.
Qiao JF, Li SY, Han HG, Wang DH (2017) An improved algorithm for building self-organizing feedforward neural networks. Neurocomputing 262:28–40
45.
Górecki T, Łuczak M (2015) Multivariate time series classification with parametric derivative dynamic time warping. Expert Syst Appl 42(5):2305–2312
46.
Jeong YS, Jeong MK, Omitaomu OA (2011) Weighted dynamic time warping for time series classification. Pattern Recognit 44(9):2231–2240
47.
Vajargah BF, Gharehdaghi M (2014) Ergodicity of fuzzy Markov chains based on simulation using sequences. Int J Appl Math Comput Sci 11(2):159–165MATH
48.
Costa GAOP, Feitosa RQ (2014) A generalized fuzzy Markov chain-based model for classification of remote-sensing multitemporal images. Int J Remote Sens 35(1):341–364
49.
Peter JEV, Dwight RP (2010) Numerical sensitivity analysis for aerodynamic optimization: a survey of approaches. Comput Fluids 39(3):373–391MathSciNetMATH
50.
Sudret B (2008) Global sensitivity analysis using polynomial chaos expansions. Reliab Eng Syst Saf 93(7):964–979
51.
Yu W, Harris TJ (2009) Parameter uncertainty effects on variance-based sensitivity analysis. Reliab Eng Syst Saf 94(2):596–603
52.
Han HG, Qiao JF (2010) A self-organizing fuzzy neural network based on a growing-and-pruning algorithm. IEEE Trans Fuzzy Syst 18(6):1129–1143
53.
Chen G, Chen Y, Ogrnen H (1997) Identifying chaotic systems via a Wiener-type cascade model. IEEE Control Syst 17(5):29–36
54.
55.
Qiao J, Wang L, Yang C (2019) Adaptive lasso echo state network based on modified Bayesian information criterion for nonlinear system modeling. Neural Comput Appl 31(10):6163–6177
56.
Wu S, Er MJ, Gao Y (2001) A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578–594
57.
Han HG, Wu XL, Qiao JF (2014) Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm. IEEE Trans Cybern 44(4):554–564
58.
Lin YY, Chang JY, Lin CT (2013) Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network. IEEE Trans Neural Netw Learn Syst 24(2):310–321
59.
Juang CF, Lin YY, Tu CC (2010) A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing. Fuzzy Sets Syst 161(19):2552–2568MathSciNet
60.
Juang CF (2002) A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms. IEEE Trans Fuzzy Syst 10(2):155–170
61.
Wang N, Er MJ, Meng XY (2009) A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks. Neurocomputing 72(16–18):3818–3829
62.
Mastorocostas PA, Theocharis JB (2002) A recurrent fuzzy-neural model for dynamic system identification. IEEE Trans Syst Man Cybern Part B Cybern 32(2):176–190
Metadata
Title
A self-organizing recurrent fuzzy neural network based on multivariate time series analysis
Authors
Haixu Ding
Wenjing Li
Junfei Qiao
Publication date
31-08-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05276-w

Other articles of this Issue 10/2021

Neural Computing and Applications 10/2021 Go to the issue

Original Article

Boosting attention fusion generative adversarial network for image denoising

S.I. : Higher Level Artificial Neural Network Based Intelligent Systems

An automated estimator for Cobb angle measurement using multi-task networks

Original Article

Regional land planning based on BPNN and space mining technology

Original Article

Prescribed-time convergent and noise-tolerant Z-type neural dynamics for calculating time-dependent quadratic programming

Original Article

CGA: a new feature selection model for visual human action recognition

S.I. : Higher Level Artificial Neural Network Based Intelligent Systems

Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization

Premium Partner

    Image Credits