A robust approximation-based event-triggered control method is presented for single input single output (SISO) nonlinear continuous-time systems with unmeasurable states and external disturbance. In the whole system, just one neural network (NN) is designed to approximate the unknown part in the controller, and output errors are directly used to construct the system controller and event-triggered mechanism to relieve the burden of system communication. The controller with a simple structure is easier to be realized in practical engineering. The system control signals and adaptive parameters are updated only if the event trigger condition is met, and this way further reduces the waste of network resources caused by frequent system sampling. The application of stability theory of Lyapunov proves that the weight estimation of the NN and tracking errors are ultimate and uniform boundedness, and the efficacy of the proposed scheme is verified with numerical results on a robot.

Multimodal medical image is an effective method to solve a series of clinical problems, such as clinical diagnosis and postoperative treatment. In this study, a medical image fusion method based on convolutional sparse representation (CSR) and mutual information correlation is proposed. In this method, the source image is decomposed into one high-frequency and one low-frequency sub-band by non-subsampled shearlet transform. For the high-frequency sub-band, CSR is used for high-frequency coefficient fusion. For the low-frequency sub-band, different fusion strategies are used for different regions by mutual information correlation analysis. Analysis of two kinds of medical image fusion problems, namely, CT–MRI and MRI–SPECT, reveals that the performance of this method is robust in terms of five common objective metrics. Compared with the other six advanced medical image fusion methods, the experimental results show that the proposed method achieves better results in subjective vision and objective evaluation metrics.