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In this paper, a new robust fuzzy inference system is utilized to predict the chaotic time series with noises or outliers. We employ an improved fuzzy rule extraction algorithm using data mining concepts to make the resulting fuzzy system more robust with respect to the input noises or outliers. And the fuzzy inference system is optimized with a partition refinement strategy so that a more suitable...
This paper aims to develop a framework of fuzzy systems for robust time-series forecasting. An improved fuzzy rule extraction algorithm using data mining concept is employed to make the resulting fuzzy system be more robust with respect to the input noises or outliers. The proposed technique in this paper is examined with comprehensive robustness analysis by a classical benchmark time-series forecasting...
Using clonal selection model identification, an adaptive PD control algorithm is proposed for uncertain dynamical systems. The practical industrial processes are dynamically identified as second-order linear model; then,a certainty equivalence principle is applied to tune the PD controller. The combination of previous elitist reservation and stochastic initialization for the initial population in...
A simple adaptive fuzzy control (SAFC) is proposed for a class of strict-feedback uncertain nonlinear systems with both unknown system nonlinearities and unknown virtual control gain nonlinearities. Combining the dynamic surface control (DSC) technique with minimal-learning-parameters (MLP) algorithm, a systematic procedure for synthesis of SAFC is developed base on the universal approximation of...
A robust adaptive fuzzy tracking control problem is discussed for a class of uncertain MIMO nonlinear systems with strongly coupled interconnections. T-S fuzzy systems are used to approximate the unknown system uncertainties. Combining ldquodynamic surface control(DSC)rdquoapproach with ldquominimal learning parameters(MLP)rdquo algorithm, a systematic procedure for controller design is developed...
An adaptive neural network control (ANNC) is proposed for a class of strict-feedback uncertain nonlinear systems with unknown system nonlinearities and unknown virtual control gain nonlinearities. Combining the dynamic surface control (DSC) technique with minimal-learning-parameters (MLP) algorithm, a systematic procedure for synthesis of ANNC is developed based on the universal approximation of neural...
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