This article introduces a novel hybrid evolutionary algorithm for recurrent fuzzy neural systems design in applications of nonlinear systems. The hybrid learning algorithm, IEMBP-improved electromagnetism-like (EM) with back-propagation (BP) technique, combines the advantages of EM and BP algorithms which provides high-speed convergence, higher accuracy and less computational complexity (computation time in seconds). In addition, the IEMBP needs only a small population to outperform the standard EM that uses a larger population. For a recurrent neural fuzzy system, IEMBP simulates the 'attraction' and 'repulsion' of charged particles by considering each neural system parameters as a charged particle. The EM algorithm is modified in such a way that the competition selection is adopted and the random neighbourhood local search is replaced by BP without evaluations. Thus, the IEMBP algorithm combines the advantages of multi-point search, global optimisation and faster convergence. Finally, several illustration examples for nonlinear systems are shown to demonstrate the performance and effectiveness of IEMBP.

This study investigates the influence of aqueous solution molarity on the structural characteristics of zinc oxide (ZnO) grown by hydrothermal method. From the X-ray diffraction (XRD) patterns of the ZnO nanostructures, the diffraction peaks confirm the ZnO hexagonal wurtzite type crystalline structure. To investigate the structural properties of ZnO structures in more detail, we analyze the XRD line profiles of the samples by Warren-Averbach model. Based on the model, the diffraction intensity of the XRD is calculated in Fourier space and the information on the size distribution can be derived. Observing the calculated nanostructure size distribution of the samples, we can see that the breadth of the size distribution function decreases then increases with increasing molarities. Furthermore, the theoretical analyzed results are verified by photoluminescence (PL) measurements and the scanning electron microscope (SEM) images.