RESUMEN
Aiming at solving non-convex nonlinear programming efficiently and accurately, a swarm exploring varying parameter recurrent neural network (SE-VPRNN) method is proposed in this article. First, the local optimal solutions are searched accurately by the proposed varying parameter recurrent neural network. After each network converges to the local optimal solutions, information is exchanged through a particle swarm optimization (PSO) framework to update the velocities and positions. The neural network searches for the local optimal solutions again from the updated position until all the neural networks are searched to the same local optimal solution. For improving the global searching ability, wavelet mutation is applied to increase the diversity of particles. Computer simulations show that the proposed method can solve the non-convex nonlinear programming effectively. Compared with three existing algorithms, the proposed method has advantages in accuracy and convergence time.
RESUMEN
We propose a novel four-ring hollow-core silicon photonic crystal fiber (PCF), and we systematically and theoretically investigate the properties of their vector modes. Our PCF can stably support 30 OAM states from the wavelength of 1.5 µm to 2.4 µm, with a large effective refractive index separation of above 1×10-4. The confinement loss is less than 1×10-9 dB/m at the wavelength of 1.55 µm, and the average confinement loss is less than 1×10-8 dB/m from the wavelength of 1.2 µm to 2.4 µm. Moreover, the curve of the dispersion tends to flatten as the wavelength increases. In addition, we comparably investigate PCFs with different hole spacing. This kind of fiber structure will be a potential candidate for high-capacity optical fiber communications and OAM sensing applications using fibers.
