“…In addition, the presence of misfit points outside the prediction band also demonstrates that the prediction accuracy of SSA-LSTM is lower than that of GWO-LSTM. In SSA, the values of ST and PD in SSA are taken with reference to the relevant literature [76] . Figure 13 The prediction performance of the SSA-LSTM (a) the measured and predicted results of SSA-LSTM; (b) the confidence band and prediction band of SSA-LSTM.…”
Section: Optimization Of Lstm and Resultsmentioning
“…In addition, the presence of misfit points outside the prediction band also demonstrates that the prediction accuracy of SSA-LSTM is lower than that of GWO-LSTM. In SSA, the values of ST and PD in SSA are taken with reference to the relevant literature [76] . Figure 13 The prediction performance of the SSA-LSTM (a) the measured and predicted results of SSA-LSTM; (b) the confidence band and prediction band of SSA-LSTM.…”
Section: Optimization Of Lstm and Resultsmentioning
“…By optimizing the SSA algorithm, the sensitivity of the initial values of control parameters was reduced and better initial values were obtained. The experimental results show that this calculation method has fast convergence speed and high robustness [14]. Liu T et al proposed a new auxiliary system in the field of brain tumor detection.…”
With the continuous improvement and development of wireless sensor network, it have been enriched to a great extent. Monitoring, processing and transmitting all kinds of sensing data is its main function, so their coverage issues have received widespread attention. Among them, the WSN coverage based on DV-Hop node positioning technology has low cost and power consumption with high scalability, and is extremely widely used. However, the current error control and WSN coverage of DV-Hop node positioning are not enough for practical applications. This research innovatively adopts an improved sparrow search pattern to optimise the DV-Hop localisation algorithm. The study introduces a deviation correction factor to adjust the minimum number of node hops and uses the minimum mean squared error criterion to correct the calculation error to reduce error. In addition, the study improved the sparrow search algorithm by means of a GPS optimisation population initialisation. In the algorithm performance comparison, GSSA showed the best convergence efficiency compared to other algorithms. The average error of the GSSADV-Hop localisation constructed in the study is 0.72 m, which is 77.71% less than the traditional DV-Hop error.The study provides a reference idea for the application of DV-Hop in WSN coverage, and offers a novel solution for the optimization of DV-Hop.
Aiming at the problem that multiple no-fly zones need to be avoided in the reentry process of aircraft, a composite programming method combining conjugate gradient and dynamic programming is proposed. The method divides the reentry flight trajectory into two parts: the longitudinal plane and the transverse plane. The longitudinal plane uses the conjugate gradient method to guide the aircraft to meet the minimum mass of the thermal protection system during the whole flight. The horizontal plane mainly uses the dynamic programming method to obtain a reasonable obstacle avoidance route and inversely calculates the change law of the heading angle. The simulation results show that the three-dimensional reentry trajectory can reasonably solve the problem of avoiding multiple no-fly zones and meeting the aerodynamic constraints of the flight process.
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