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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2019

21-03-2018 | Original Article

Adaptive lasso echo state network based on modified Bayesian information criterion for nonlinear system modeling

Authors: Junfei Qiao, Lei Wang, Cuili Yang

Published in: Neural Computing and Applications | Issue 10/2019

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Abstract

Echo state network (ESN), a novel recurrent neural network, has a randomly and sparsely connected reservoir. Since the reservoir size is very large, the collinearity problem may exist in the ESN. To address this problem and get a sparse architecture, an adaptive lasso echo state network (ALESN) is proposed, in which the adaptive lasso algorithm is used to calculate the output weights. The ALESN combines the advantages of quadratic regularization and adaptively weighted lasso shrinkage; furthermore, it has the oracle properties and can deal with the collinearity problem. Meanwhile, to obtain the optimal model, the selection of tuning regularization parameter based on modified Bayesian information criterion is proposed. Simulation results show that the proposed ALESN has better performance and relatively uniform output weights than some other existing methods.
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Literature
1.
Han HG, Wu XL, Qiao JF (2013) Real-time model predictive control using a self-organizing neural network. IEEE Trans Neural Netw Learn Syst 24(9):1425–1436CrossRef
2.
Zhang YW, Chai TY, Li ZM, Yang CY (2012) Modeling and monitoring of dynamic processes. IEEE Trans Neural Netw Learn Syst 23(2):277–284CrossRef
3.
Qiao JF, Han HG (2012) Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach. Automatica 48(8):1729–1734MathSciNetCrossRef
4.
Chang WD (2014) Recurrent neural network modeling combined with bilinear model structure. Neural Comput Appl 24(3–4):765–773CrossRef
5.
Han HG, Wu XL, Qiao JF (2014) Nonlinear system modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm. IEEE Trans Cybern 44(4):554–564CrossRef
6.
Li FJ, Qiao JF, Han HG, Yang CL (2016) A self-organizing cascade neural network with random weights for nonlinear system modeling. Appl Soft Comput 42:184–193CrossRef
7.
Han HG, Zhang S, Qiao JF (2017) An adaptive growing and pruning algorithm for designing recurrent neural network. Neurocomputing 242:51–62CrossRef
8.
Hwang CL, Jan C (2016) Recurrent neural network based multivariable adaptive control for a class of nonlinear dynamic systems with time-varying delay. IEEE Trans Neural Netw Learn Syst 27(2):388–401MathSciNetCrossRef
9.
Geng K, Marmarelis VZ (2017) Methodology of recurrent Laguerre-Volterra network for modeling nonlinear dynamic systems. IEEE Trans Neural Netw Learn Syst 28(9):2196–2208MathSciNet
10.
Wang L, Zhang L, Yi Z (2017) Trajectory predictor by using recurrent neural networks in visual tracking. IEEE Trans Cybern 47(10):3172–3183CrossRef
11.
Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667):78–80CrossRef
12.
Bacciu D, Barsocchi P, Chessa S et al (2014) An experimental characterization of reservoir computing in ambient assisted living applications. Neural Comput Appl 24(6):1451–1464CrossRef
13.
Qiao JF, Li FJ, Han HG, Li WJ (2017) Growing echo state network with multiple subreservoirs. IEEE Trans Neural Netw Learn Syst 28(2):391–404CrossRef
14.
Peng Y, Lei M, Li JB et al (2014) A novel hybridization of echo state networks and multiplicative seasonal ARIMA model for mobile communication traffic series forecasting. Neural Comput Appl 24(3–4):883–890CrossRef
15.
Oubbati M, Palm G (2010) A neural framework for adaptive robot control. Neural Comput Appl 19(1):103–114CrossRef
16.
Skowronski MD, Harris JG (2007) Automatic speech recognition using a predictive echo state network classifier. Neural Netw 20(3):414–423CrossRef
17.
Xia Y, Jelfs B, Van Hulle MM (2011) An augmented echo state network for nonlinear adaptive filtering of complex noncircular signals. IEEE Trans Neural Netw 22(1):74–83CrossRef
18.
Xu M, Han M (2016) Adaptive elastic echo state network for multivariate time series prediction. IEEE Trans Cybern 46(10):2173–2183CrossRef
19.
Dutoit X, Schrauwen B, Van Campenhout J, Stroobandt D, Van Brussel H, Nuttin M (2009) Pruning and regularization in reservoir computing. Neurocomputing 72(7–9):1534–1546CrossRef
20.
Reinhart RF, Steil JJ (2012) Regularization and stability in reservoir networks with output feedback. Neurocomputing 90:96–105CrossRef
21.
Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B 58(1):267–288MathSciNetMATH
22.
Donoho DL, Huo X (2001) Uncertainty principles and ideal atomic decomposition. IEEE Trans Inf Theory 47(7):2845–2862MathSciNetCrossRef
23.
Meinshausen N, Bühlmann P (2006) High-dimensional graphs and variable selection with the lasso. Ann Statist 34(3):1436–1462MathSciNetCrossRef
24.
25.
Kwon S, Lee S, Na O (2017) Tuning parameter selection for the adaptive lasso in the autoregressive model. J Kor Stat Soc 46(2):285–297MathSciNetCrossRef
26.
Shibata R (1976) Selection of the order of an autoregressive model by Akaike’s information criterion. Biometrika 63(1):117–126MathSciNetCrossRef
27.
28.
Jaeger H (2001) The ‘echo state’ approach to analysing and training recurrent neural networks-with an erratum note. German Nat Res Center Inf Technol, Bonn, Germany, Tech Rep 148
29.
30.
Hom RA, Johnson CR (1991) Topics in matrix analysis. Cambridge University Press, New York
31.
Wang HS, Yan XF (2014) Improved simple deterministically constructed cycle reservoir network with sensitive iterative pruning algorithm. Neurocomputing 145:353–362CrossRef
32.
Zhang L, Li K (2015) Forward and backward least angle regression for nonlinear system identification. Automatica 53:94–102MathSciNetCrossRef
33.
Chen H, Gong Y, Hong X (2013) Online modeling with tunable RBF network. IEEE Trans Cybern 43(3):935–947CrossRef
34.
Han HG, Lu W, Hou Y (2018) An Adaptive-PSO-based self-organizing RBF neural network. IEEE Trans Neural Netw Learn Syst 29(1):104–117MathSciNetCrossRef
35.
36.
Jaeger H (2005) Reservoir riddles: suggesting for echo state network research. IEEE Int J Conf Neural Netw Montreal, Canada, pp 1460–1462
37.
Ferreira AA, Ludermir TB, De Aquino RRB (2013) An approach to reservoir computing design and training. Expert Syst Appl 40(10):4172–4182CrossRef
38.
Malik ZK, Hussain A, Wu QJ (2017) Multilayered echo state machine: a novel architecture and algorithm. IEEE Trans Cybern 47(4):946–959CrossRef
39.
40.
41.
Rodan A, Tiňo P (2011) Minimum complexity echo state network. IEEE Trans Neural Netw 22(1):131–144CrossRef
42.
Han HG, Zhang L, Hou Y, Qiao JF (2016) Nonlinear model predictive control based on a self-organizing recurrent neural network. IEEE Trans Neural Netw Learn Syst 27(2):402–415MathSciNetCrossRef
Metadata
Title
Adaptive lasso echo state network based on modified Bayesian information criterion for nonlinear system modeling
Authors
Junfei Qiao
Lei Wang
Cuili Yang
Publication date
21-03-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3420-6

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