This paper proposes a new general recurrent state-space neuro-fuzzy model structure. Three topologies are under assessment, including the state-input recurrent neuro-fuzzy system, the series-parallel recurrent neuro-fuzzy system and the parallel recurrent neuro-fuzzy system. Moreover, the underlying generalised state-space Takagi–Sugeno system is proven to be a universal approximator, and some stability conditions derived for this system. The online training is carried out based on a constrained unscented Kalman filter, where weights, membership functions and consequents are recursively updated. Results from experiments on a benchmark MIMO system demonstrate the applicability and flexibility of the proposed system identification approach.
Nonlinear system identification Takagi–Sugeno models Neuro-fuzzy systems Unscented transform Kalman filter
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All authors declare that they have no conflicts of interest.
This article does no contain any studies with human participants or animals performed by any of the authors.
Ariño C, Sala A, Pérez E, Bedate F, Querol A (2017) Asymptotically exact stabilisation for constrained discrete Takagi–Sugeno systems via set-invariance. Fuzzy Sets Syst 316:117–138 (theme: control engineering)Google Scholar
Bourahala F, Guelton K, Manamanni N, Khaber F (2016) Relaxed controller design conditions for Takagi–Sugeno systems with state time-varying delays. Int J Fuzzy Syst 1–11Google Scholar
Chang Y-C, Chen C-H, Zhu Z-C, Huang Y-W (2016a) Speed control of the surface-mounted permanent-magnet synchronous motor based on Takagi–Sugeno fuzzy models. IEEE Trans Power Electron 31(9):6504–6510CrossRefGoogle Scholar
Chang P-C, Wu J-L, Lin J-J (2016b) A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting. Appl Soft Comput 38:831–842CrossRefGoogle Scholar
Dash R, Dash PK (2016) Efficient stock price prediction using a self evolving recurrent neuro-fuzzy inference system optimized through a modified technique. Exp Syst Appl 52:75–90CrossRefGoogle Scholar
Dunik J, Simandl M, Straka O (2012) Unscented Kalman filter: aspects and adaptive setting of scaling parameter. IEEE Trans Autom Control 57(9):2411–2416MathSciNetCrossRefzbMATHGoogle Scholar
Feng Z, Zheng WX (2017) Improved stability condition for Takagi–Sugeno fuzzy systems with time-varying delay. IEEE Trans. Cybern. 47(3):661–670CrossRefGoogle Scholar
Gao Q (2017) Universal fuzzy models and universal fuzzy controllers for stochastic non-affine nonlinear systems. In: Universal fuzzy controllers for non-affine nonlinear systems. Springer, Singapore, pp 45–70Google Scholar
Gao Q, Zeng X-J, Feng G, Wang Y, Qiu J (2012) T–S-fuzzy-model-based approximation and controller design for general nonlinear systems. IEEE Trans Syst Man Cybern B Cybern 42(4):1143–1154CrossRefGoogle Scholar
Gao Q, Feng G, Dong D, Liu L (2015) Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems. IEEE Trans Cybern 45(5):880–887CrossRefGoogle Scholar
Husek P (2016) On monotonicity of Takagi–Sugeno fuzzy systems with ellipsoidal regions. IEEE Trans Fuzzy Syst 24(6):1673–1678CrossRefGoogle Scholar
Li L, Yu D, Xia Y, Yang H (2017) Stochastic stability of a modified unscented Kalman filter with stochastic nonlinearities and multiple fading measurements. J Franklin Inst 354(2):650–667MathSciNetCrossRefzbMATHGoogle Scholar
Liu Y, Lee S (2016) Stability and stabilization of Takagi–Sugeno fuzzy systems via sampled-data and state quantized controller. IEEE Trans Fuzzy Syst 24(3):635–644Google Scholar
Lin G, Zhao K, Wan Q (2016) Takagi–Sugeno fuzzy model identification using coevolution particle swarm optimization with multi-strategy. Appl Intell 1–11Google Scholar