Flexible robot system is in general taken into real consideration as most important process in a number of academic and industrial environments. Due to the fact that the aforementioned system is so applicable in real domains, the novel ideas with respect to state-of-the-art in outperforming its performance are always valuable. With this purpose, a number of the soft computing techniques can be preferred with reference to the traditional ones to predict and optimize the overall performance of the above-captioned process. The approach proposed here is in fact organized in line with the integration of the fuzzy-based approach in association with the neural networks, in order to enable the process under control to be capable of learning and adapting to be matched, in a number of real environments. It can be shown that the outcomes tolerate the imprecise circumstances, as one of advantages regarding the fuzzy-based approach. In the present investigation, a new hybrid approach is proposed to deal with the arm of flexible robot system through the neural networks, the fuzzy-based approach and also the particle swarm optimization. It should be noted that the objective of the proposed research is to control the claw of robot system including two-degree-of-freedom movable arms. The results indicate that the mean-square error and the root-mean-square error are accurately outperformed with reference to the traditional ones, tangibly.
Fiber optic transmission systems are friendly data transmission systems environmentally due to their low losses, high bandwidth and acceptable reliability. Therefore, they are often used for communication infrastructures. However, the effects of optical loss and dispersion can cause problems in fiber optic communications. In this paper, we mitigate these effects by using photonic crystals. The goal is to minimise the dispersion of the fibers by changing the photonic crystal fiber parameters. Specifically, we change the number, radius, and shape of the holes to minimise dispersion. A new photonic crystal fiber (PCF) is proposed with hexagonal grids of air holes and 11 layers in this structure. An optimal holes number, radius and distance between holes are used to obtain the least dispersion. Next, we use several elliptical and stellar holes to reduce dispersion. The germanium impurity is exploited in the PCF core as a defect. The addition of germanium impurity to the core causes the doping atoms reflect a stronger optical signal with the same attenuated input signal properties. The simulation results show that dispersion value is zero in three points at wavelengths between 1.48 and 1.55 μm. In this wavelength range, the dispersion value was obtained between − 0.3 and 0.6 ps/(nm km).
In this paper, a new structure of photonic crystal fibers (PCFs) with large negative dispersion is presented in order to compensate the positive dispersion at a wavelength of 1.55 μm. The proposed PCF has square lattice structure. In the basic structure of the cladding, it consists of four square rings, so that each ring has a number of circular holes. In the final proposed structure, the two inner rings of the cladding are reshaped relative to the two outer rings. The first inner ring near the core has stellar shaped holes with gold nanoparticles in its center, and the second inner ring has circular holes with a smaller diameter than the two outer rings. The base material of this fiber is silica. The use of stellar shaped holes with gold nanoparticles cores causes a large amount of negative dispersion, which compensates the positive dispersion. Simulation results show that the minimum dispersion is -4593 ps/(nm.km) at a wavelength of 1.55 μm which bring a significant improvement compared to other similar references.
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