Data classification based on fractional order gradient descent with momentum for RBF neural network.

The weight-updating methods have played an important role in improving the performance of neural networks. To ameliorate the oscillating phenomenon in training radial basis function (RBF) neural network, a fractional order gradient descent with momentum method for updating the weights of RBF neural network (FOGDM-RBF) is proposed for data classification. Its convergence is proved.

Time-Delayed Fractional Order Adaptive Sliding Mode Control for Two-Wheel Self-Balancing Vehicles.

In this paper, a time-delayed fractional order adaptive sliding mode control algorithm is proposed for a two-wheel self-balancing vehicle system. The closed-loop system is proved based on the Lyapunov-Razumikhin function. The switching function is designed to make the system robust when facing uncertainties and external disturbances.

Experimental Realization of an Extreme-Parameter Omnidirectional Cloak.

An ideal transformation-based omnidirectional cloak always relies on metamaterials with extreme parameters, which were previously thought to be too difficult to realize. For such a reason, in previous experimental proposals of invisibility cloaks, the extreme parameters requirements are usually abandoned, leading to inherent scattering. Here, we report on the first experimental demonstration of an omnidirectional cloak that satisfies the extreme parameters requirement, which can hide objects in a homogenous background.

Manipulating surface plasmon polaritons with infinitely anisotropic metamaterials.

Guiding surface states through disorders recently has attracted attention of scientists from diverse backgrounds. In this work, we report a robust method to guide surface plasmon polaritons (SPPs) through arbitrary distorted metal surfaces (a kind of disorder), including slopes, bumps, and sharp corners. Almost total transmissions over a broad frequency range can be achieved by use of infinitely anisotropic metamaterials (IAMs).