Shigang Cui

Topology identification of uncertain nonlinearly coupled complex networks with delays based on anticipatory synchronization

This paper presents an adaptive anticipatory synchronization based method for simultaneous identification of topology and parameters of uncertain nonlinearly coupled complex dynamical networks with time delays. An adaptive controller is proposed, based on Lyapunov stability theorem and Barbǎlats Lemma, to guarantee the stability of the anticipatory synchronization manifold between drive and response networks. Meanwhile, not only the identification criteria of network topology and system parameters are obtained but also the anticipatory time is identified. Numerical simulation results illustrate the effectiveness of the proposed method.

Parameter estimation of the FitzHugh-Nagumo model using noisy measurements for membrane potential.

This paper proposes an identification method to estimate the parameters of the FitzHugh-Nagumo (FHN) model for a neuron using noisy measurements available from a voltage-clamp experiment. By eliminating an unmeasurable recovery variable from the FHN model, a parametric second order ordinary differential equation for the only measurable membrane potential variable can be obtained. In the presence of the measurement noise, a simple least squares method is employed to estimate the associated parameters involved in the FHN model. Although the available measurements for the membrane potential are contaminated with noises, the proposed identification method aided by wavelet denoising can also give the FHN model parameters with satisfactory accuracy. Finally, two simulation examples demonstrate the effectiveness of the proposed method.

H ∞ controller design for a ball and plate system using normalized coprime factors

In this paper, we discuss how to synthesize a stabilizably robust controller for a ball and plate system (BPS). In order to reduce the fragility in its robust controller synthesis, a loop-shaping method in terms of a normalized right coprime factor (NRCF) uncertainty description for the BPS is thus employed. In effect, the design of weighting functions for the BPS loop-shaping is a decisive factor to determine good performance of the resulting closed-loop system. As a result, the well-known lead-lag series compensation design methods are innovatively adopted to obtain appropriate pre and post compensators as the weighting functions to guaranteed the BPS time-domain performance requirements. Finally, a numerical simulation on the BPS is used to verify the effectiveness of the proposed design method for the shaping weighting functions.

Design of a Temperature and Humidity Monitoring System for Plant Growth Cabinets Based on Data Fusion

This paper developed a temperature and humidity monitoring system for a plant growth cabinet based on the embedded technology.The monitoring system took a STM32 microprocessor as its core controller, and the accurate acquisition, display and control of environmental factors (including temperature, humidity) in the plant growth cabinet were achieved by the monitoring system. In order to solve the problem of low accuracy of such monitoring system, the means and variances of the temperature and humidity data after fusion were obtained from a batch estimation fusion method based on average values. Then an adaptive weighted fusion was applied on each layer of sensor data in accordance with the optimal weight distribution principle to obtain accurate temperature and humidity values. The testing results shows that the system is quite stable and reliable, which can monitor and display the real-time temperature and humidity existed in the plant growth cabinet accurately and satisfy the needs for control.

Synchronization analysis of EEG signals evoked by manual acupuncture

Manual acupuncture (MA), as a mechanical action, can be equivalent to an external stimulus to the neural system. However, the regulation and action mechanism of acupuncture is still unclear. In the present study, synchronization levels of brain activities evoked by MA is investigated. We design an experiment that acupuncture at Zusanli with four different frequencies to obtain electroencephalograph (EEG) signals. Since neural system is a complex nonlinear dynamics system that possesses strong nonlinear characteristic, phase synchronization (PS) based on recurrence plot method is introduced to quantify the synchronization between EEG signals induced by MA. It is found that the synchronization between different brain regions during acupuncture is obviously lower than that before acupuncture, which can prove that MA has effect on the brain.

Topology Estimation of Uncertain General Complex Dynamical Networks from Noisy Time Series

This paper addresses the problem of simultaneous estimation of the topological structure and unknown parameters of uncertain general complex networks from noisy time series. Usually the complex networks consist of known node models with some unknown parameters and uncertain topological structure. At the same time, only partial states with heavy noise can be observed in real-world complex networks. By means of the unscented Kalman filter (UKF), we estimate the unknown states, parameters as well as topological structure with high accuracy only from partial heavily noise-corrupted states of the nodes. The simulation results verify the effectiveness of the proposed approach.

Chaos Synchronization of Coupled FitzHugh-Nagumo Neurons Via Adaptive Sliding Mode Control

In this paper, an adaptive neural network (NN) sliding mode controller is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. The controller consists of two simple radial basis function (RBF) NNs which are used to approximate the desired sliding mode controller and the uncertain nonlinear part of the error dynamical system, respectively. The weights of these NNs are tuned on-line based on the sliding mode reaching law. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as approximate error, ionic channel noise and external disturbances. Chaos synchronization are obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.