Showing papers by "Yang Gao published in 2003"

Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems

Abstract: This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.

Robust adaptive control of robot manipulators using generalized fuzzy neural networks

Abstract: This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for motion control of multilink robot manipulators. The proposed controller has the following salient features: (1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically according to their significance to the control system and the complexity of the mapped system and no predefined fuzzy rules are required; (2) fast online learning ability, i.e., no prescribed training models are needed for online learning and weights of the fuzzy neural controller are modified without any iterations; (3) fast convergence of tracking errors, i.e., manipulator joints can track the desired trajectories very quickly; (4) adaptive control, i.e., structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; and (5) robust control, where asymptotic stability of the control system is established using the Lyapunov theorem. Experimental evaluation conducted on an industrial selectively compliant assembly robot arm demonstrates that excellent tracking performance can be achieved under time-varying conditions.

Feature extraction using wavelet packets strategy

Abstract: In this paper, improved wavelet packets (WPs) decomposition coefficients of the frame are applied in the feature extraction method. In the proposed speech recognition system, the static WPs coefficients+dynamic WPs coefficients of the frame were employed as a basic feature. The framework of linear discriminant analysis (LDA) is used to derive an efficient and reduced-dimension speech parametric vector space for the speech recognition system. Using the continuous hidden Markov model (HMM) as the speech recognition model, the speech recognition system was successfully constructed. Experiments are performed on the speaker independent isolated-word speech recognition task. It is found that the improved WPs method achieves better recognition performance than the most popular Mel frequency cepstral coefficients (MFCC) feature extraction method in a noisy environment.

Modelling, control, and stability analysis of non-linear systems using generalized fuzzy neural networks

Abstract: This paper presents an adaptive fuzzy neural controller (AFNC) suitable for modelling and control of MIMO non-linear dynamic systems. The proposed AFNC has the following salient features: (1) fuzzy neural control rules can be generated or deleted dynamically and automatically; (2) uncertain MIMO non-linear systems can be adaptively modelled on line; (3) adaptation and learning speed is fast; (4) expert knowledge can be easily incorporated into the system; (5) the structure and parameters of the AFNC can be self-adaptive in the presence of uncertainties to maintain a high control performance; and (6) the asymptotical stability of the system is established using the Lyapunov approach. Simulation studies on a two-link robot manipulator show that the performance of the proposed controller is better than that of some existing fuzzy/neural methods.

An adaptive RBFN-based filter for adaptive noise cancellation

Abstract: In this paper, a new adaptive radial-basis-function-networks- (RBFN-) based filter for the adaptive noise cancellation (AXC) problem is proposed. The algorithm of structure identification and parameters adjustment is developed. The proposed RBFN-based filtering approach implements Takagi-Sugeno-Kang (TSK) fuzzy systems functionally. The RBFN-based filter has three major features: (1) no space pre-partitioning is needed; (2) no predetermination, such as the number of RBF neurons (fuzzy rules), must be given; (3) fast learning speed is achieved. Simulation results demonstrate that the proposed adaptive RBFN-based filter can cancel the noise successfully and efficiently with a parsimonious structure.

Online adaptive fuzzy neural identification and control of nonlinear dynamic systems

Abstract: This chapter presents a robust Adaptive Fuzzy Neural Controller (AFN C) suitable for identification and control of uncertain Multi-Input-Multi-Output (MIMO) nonlinear systems. The proposed controller has the following salient features: (1) Self-organizing fuzzy neural structure, i.e. fuzzy control rules can be generated or deleted automatically; (2) Online learning ability of uncertain MIMO nonlinear systems; (3) Fast learning speed; (4) Fast convergence of tracking errors; (5) Adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; (6) Robust control, where global stability of the system is established using the Lyapunov approach. Two simulation examples are used to demonstrate excellent performance of the proposed controller.

Adaptive control strategy for blood pressure regulation using a fuzzy neural network

Abstract: This paper presents an adaptive fuzzy neural control strategy to regulate Mean Arterial Pressure (MAP) through the intravenous infusion of Sodium NitroPrusside (SNP). The proposed indirect adaptive controller involves a feedforward Generalized Fuzzy Neural Network (G-FNN) together with a linear feedback loop. It is capable of achieving real-time fine control under significant uncertainties and without any prior knowledge of the system dynamics. This is achieved through adaptive learning and modeling of the system dynamics and its uncertainties based on the G-FNN. Salient features of the proposed G-FNN include dynamic fuzzy neural structure, fast online learning ability and adaptability, etc. Simulation studies demonstrate the superior performance of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

Adaptive fuzzy neural control of mean arterial pressure through sodium nitroprusside infusion

Abstract: This paper presents an adaptive modeling and control scheme for drug delivery systems based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model the unknown nonlinearities of complex drug delivery systems and adapt on line to changes and uncertainties in these systems. It offers salient features, such as dynamic fuzzy neural topology, fast online learning ability and adaptability, etc. System approximation formulated by the G-FNN is thus employed in the adaptive controller design for drug infusion. In particular, this paper investigates the automated regulation of mean arterial pressure (MAP) through the intravenous infusion of sodium nitroprusside (SNP), which is atypical application in automation of drug delivery. Simulation study demonstrates the ability of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

NARMAX-model-based time series modeling and prediction: feedforward and recurrent fuzzy neural network approaches

Abstract: The nonlinear autoregressive moving average with exogenous inputs (NARMAX) model provides a powerful representation for time series analysis, modeling and prediction due to its strength to accommodate the dynamic, complex and nonlinear nature of real time series applications. This paper focuses on the modeling and prediction of NARMAX-model-based time series using the fuzzy neural network (FNN) methodology with an extention of the model represention include feedforward and recurrent FNNs. This paper introduces and develops a efficient algorithm, namely generalized fuzzy neural network (G-FNN) learning algorithm, for model structure determination and parameter identification with the aim of producing improved predictive performance for NARMAX time series models. Experiments and comparisons demonstrate that the proposed G-FNN approaches can effectively learn complex temporal sequences in an adaptive way and outperform some well-known existing methods.

Adaptive fuzzy neural modeling and control scheme for mean arterial pressure regulation

Abstract: This paper presents an adaptive modeling and control scheme for blood pressure regulation based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model the unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural topology, fast online learning ability and adaptability, etc. System approximation formulated by the G-FNN is thus employed in the adaptive control of drug infusion for blood pressure regulation. In particular, this paper investigates automated regulation of mean arterial pressure (MAP) through the intravenous infusion of sodium nitroprusside (SNP), which is one of the most attractive applications in automation of drug delivery. Simulation study demonstrates superior performance of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

The CLEO III silicon vertex detector

Abstract: The design and operation of the CLEO III silicon vertex detector is described in this report. This detector consists of four layers of double-sided silicon wafers covering 93% of the solid angle. After initially meeting its signal-to-noise and spatial resolution design goals, the r − φ side efficiency of layers 1 and 2 decreased dramatically due to radiation-induced sensor effects.