Guang-She Zhao

Extended sequential adaptive fuzzy inference system for classification problems

This paper presents the performance evaluation of the recently developed Sequential Adaptive Fuzzy Inference System (SAFIS) algorithm for classification problems. In SAFIS the number of fuzzy rules can be automatically determined according to learning process and the parameters in fuzzy rules can be updated simultaneously. Earlier SAFIS has been evaluated only for function approximation problems. Improvements to SAFIS for enhancing its performance in both accuracy and speed are described in the paper and the resulting algorithm is referred to as Extended SAFIS (ESAFIS). In ESAFIS, the concept of the modified influence of a fuzzy rule is introduced for adding or removing the fuzzy rules. If the input data does not warrant adding of fuzzy rules, the parameters of the fuzzy rules are updated using a Recursive Least Square Error (RLSE) scheme. Empirical study of ESAFIS is executed based on several commonly used classification benchmark problems. The results indicate that the proposed ESAFIS produces higher classification accuracy with reduced computational complexity compared with original SAFIS and other algorithms, such as eTS (Angelov and Filev 2004), Simpl_eTS (Angelov and Filev 2005) and k-NN (Huang etxa0al. 2006).

Stable indirect adaptive neural controller for a class of nonlinear system

Abstract The paper presents an indirect adaptive neural control scheme for a general high-order nonlinear continuous system. In the proposed scheme a neural controller is constructed based on the single-hidden layer feedforward network (SLFN) for approximating the unknown nonlinearities of dynamic systems. A sliding mode controller is also incorporated to compensate for the modelling errors of SLFN. The parameters of the SLFN are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where the parameters of the hidden nodes are assigned randomly. However different from the original ELM algorithm, the output weights are updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system, even in the presence of modelling errors which are offset using the sliding mode controller. Finally the proposed adaptive neural controller is applied to control the inverted pendulum system with two different reference trajectories. The simulation results demonstrate that good tracking performance is achieved by the proposed control scheme.

Adaptive fuzzy control of aircraft wing-rock motion

In the paper, two adaptive fuzzy control schemes including indirect and direct frameworks are developed for suppressing the wing-rock motion that is a highly nonlinear aerodynamic phenomenon in which limit cycle roll oscillations are experienced by aircraft at high angles of attack. In the two control topologies, a dynamic fuzzy system called Extended Sequential Adaptive Fuzzy Inference System (ESAFIS) is constructed to represent the dynamics of the wing-rock system. ESAFIS is an online learning fuzzy system in which the rules are added or deleted based on the input data. In the indirect control scheme, the ESAFIS is used to estimate the nonlinear dynamic function and then a stable indirect fuzzy controller is designed based on the estimator. In the direct control scheme, the ESAFIS controller is directly designed to imitate an ideal stable control law without determining the model of the dynamic function. Different from the original ESAFIS, the adaptive tuning algorithms for the consequent parameters are established in the sense of Lyapunov theorem to ensure the stability of the overall control system. A sliding mode controller is also designed to compensate for the modelling errors of ESAFIS by augmenting the indirect/direct fuzzy controller. Finally, comparisons with a neuron control scheme using the RBF network and a fuzzy control scheme with Takagi-Sugeno (TS) system are presented to depict the effectiveness of the proposed control strategies. Simulation results show that the proposed fuzzy controllers achieve better tracking performance with dynamically allocating the rules online.

Iterative identification of block-oriented nonlinear systems based on biconvex optimization

Abstract We investigate the identification of a class of block-oriented nonlinear systems which is represented by a common model in this paper. Then identifying the common model is formulated as a biconvex optimization problem. Based on this, a normalized alterative convex search (NACS) algorithm is proposed under a given arbitrary nonzero initial condition. It is shown that we only need to find the unique partial optimum point of a biconvex cost function in order to obtain its global minimum point. Thus, the convergence property of the proposed algorithm is established under arbitrary nonzero initial conditions. By applying the results to Hammerstein–Wiener systems with an invertible nonlinear function, the long-standing problem on the convergence of iteratively identifying such systems under arbitrary nonzero initial conditions is also now solved.

Adaptive neural control for a class of MIMO nonlinear systems with extreme learning machine

This paper presents two adaptive neural control schemes for a class of uncertain continuous-time multi-input multi-output (MIMO) nonlinear dynamic systems. Within these schemes, the single-hidden layer feedforward networks (SLFNs) are applied to approximate the unknown nonlinear functions of the systems and then the neural controller is built based on the approximated neural models. The parameters of the SLFNs are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where the parameters of the hidden nodes are assigned randomly. Different from the original ELM algorithm, the output weights are updated using the adaptive laws derived based on the Lyapunov stability theorem and Barbalats lemma so that the asymptotical stability of the system can be guaranteed. The robustifying control term is also constructed to compensate for approximation errors of the SLFNs. In order to avoid the requirement of the approximation error bounds, the estimation laws derived based on the Lyapunov stability theorem and Barbalats lemma are employed to estimate the error bounds in the second adaptive control scheme. Finally the proposed control schemes are applied to control a two-link robot manipulator. The simulation results demonstrate the effectiveness of the proposed control schemes for the MIMO nonlinear system.

Direct adaptive neural control of nonlinear systems with extreme learning machine

A direct adaptive neural control scheme for a class of nonlinear systems is presented in the paper. The proposed control scheme incorporates a neural controller and a sliding mode controller. The neural controller is constructed based on the approximation capability of the single-hidden layer feedforward network (SLFN). The sliding mode controller is built to compensate for the modeling error of SLFN and system uncertainties. In the designed neural controller, its hidden node parameters are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where they are assigned random values. However, different from the original ELM algorithm, the output weight is updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system. The proposed adaptive neural controller is finally applied to control the inverted pendulum system with two different reference trajectories. The simulation results demonstrate good tracking performance of the proposed control scheme.

Aircraft recognition using modular extreme learning machine

In this paper, a novel recognition scheme is proposed for identifying the aircrafts of different types based on multiple modular neural network classifiers. Three moment invariants including Hu moments, Zernike moments and Wavelet moments are extracted from the characteristics exhibited by aircrafts and used as the input variables of each modular neural network respectively. Each modular neural network consists of multiple single-hidden layer feedforward networks which are trained using the extreme learning machine and different clustering data subsets. A clustering and selection method is used to get the classification rate of each modular neural network and then based on their weighted sum the final classification output is obtained. The proposed recognition scheme is finally evaluated by recognizing six different types of aircraft models and the simulation results show the superiority of the proposed method compared with the single ELM classifier and other classification algorithms.

Trace Ratio Criterion for Feature Extraction in Classification

A generalized linear discriminant analysis based on trace ratio criterion algorithm (GLDA-TRA) is derived to extract features for classification. With the proposed GLDA-TRA, a set of orthogonal features can be extracted in succession. Each newly extracted feature is the optimal feature that maximizes the trace ratio criterion function in the subspace orthogonal to the space spanned by the previous extracted features.

A novel meta-cognitive fuzzy-neural model with backstepping strategy for adaptive control of uncertain nonlinear systems

A significant increase of system complexity and state changes requires an effective data-driven system identification and machine learning algorithm to deal with the control of nonlinear systems. Using streams of data collected from the system, the data-driven controller aims to stabilize the unknown nonlinear systems with modeling uncertainties and external disturbances. The paper proposes a novel data-driven adaptive control approach with the backstepping strategy for online control of unknown nonlinear systems with no human intervention. A new meta-cognitive fuzzy-neural model is first introduced to construct the unknown system dynamics and utilize the self-adaptive tracking error as the learning parameters to determine the deletion of the state data, adapt the structure and parameters of the controller using the information extracted from nonstationary data streams. Subsequently, the control law is constructed based on the meta-cognitive fuzzy-neural model rather than the actual systems and the backstepping control strategy. Then, the stability analysis of the closed-loop system is presented from the Lyapunov function and shows that the tracking errors converge to zero. In the proposed control scheme, the bound of the control input is considered and ensured via the stable projection-type adaptation laws of the parameters. Moreover, in order to further save online computation time, only the parameters of the rule nearest to the current state are updated while those of other rules maintain unchanged. This is different from the existing studies where the parameters of all rules are updated. Finally, various simulation results from an inverted pendulum system and a thrust active magnetic bearing system demonstrate the superior performance of the proposed meta-cognitive fuzzy-neural control approach.

Adaptive backstepping control for magnetic bearing system via feedforward networks with random hidden nodes

This paper presents an adaptive backstepping neural controller (ABNC) to achieve precise rotor position tracking for a nonlinear active magnetic bearing (AMB) system with modeling uncertainties and external disturbances. In the proposed ABNC, the single-hidden layer feedforward networks (SLFNs) are used to approximate the unknown nonlinearities of dynamic systems and then based on the approximated models, the neural controller is constructed. Different from the existing methods, the hidden node parameters of the SLFNs are determined using the recently proposed neural algorithm named extreme learning machine (ELM), where these parameters are assigned randomly without adjusting. This simplifies the controller design process. Using the Lyapunov theory, stable tuning rules are derived for the update of the output weights of the SLFNs and a proof of stability in the uniformly bounded sense is given for the resulting controller. Moreover, to relax the online computation burden existing in the ABNC, a simplified ABNC (Simpl_ABNC) with less parameters to be adjusted online is proposed to improve the control performance. Finally the simulation results demonstrate that the proposed ABNC and Simpl_ABNC achieve better tracking performance comparing with other controllers including PID controller, conventional backstepping controller and adaptive backstepping sliding mode controller. Also the results show that the Simpl_ABNC has much less computation complexity and also better tracking performance than the ABNC.