https://www.selleckchem.com/products/tp-1454.html The stability and convergence of the system are analyzed and verified. Finally, simulation examples are used to verify the effectiveness of the proposed method and compare it with existing methods to confirm its superior performance.In this brief, the problem of synchronization control is investigated for a class of fractional-order chaotic systems with unknown dynamics and disturbance. The controller is constructed using neural approximation and disturbance estimation where the system uncertainty is modeled by neural network (NN) and the time-varying disturbance is handled using disturbance observer (DOB). To evaluate the estimation performance quantitatively, the serial-parallel estimation model is constructed based on the compound uncertainty estimation derived from NN and DOB. Then, the prediction error is constructed and employed to design the composite fractional-order updating law. The boundedness of the system signals is analyzed. The simulation results show that the proposed new design scheme can achieve higher synchronization accuracy and better estimation performance.Nonnegative matrix factorization (NMF) and spectral clustering are two of the most widely used clustering techniques. However, NMF cannot deal with the nonlinear data, and spectral clustering relies on the postprocessing. In this article, we propose a Robust Matrix factorization with Spectral embedding (RMS) approach for data clustering, which inherits the advantages of NMF and spectral clustering, while avoiding their shortcomings. In addition, to cluster the data represented by multiple views, we present the multiview version of RMS (M-RMS), and the weights of different views are self-tuned. The main contributions of this research are threefold 1) by integrating spectral clustering and matrix factorization, the proposed methods are able to capture the nonlinear data structure and obtain the cluster indicator directly; 2) instead of using the