In order to predict the temperature change of Laoshan scenic area in Qingdao more accurately, a new back propagation neural network (BPNN) prediction model is proposed in this study. Temperature change affects our lives in various ways. The challenge that neural networks tend to fall into local optima needs to be addressed to increase the accuracy of temperature prediction. In this research, we used an improved genetic algorithm (GA) to optimize the weights and thresholds of BPNN to solve this problem. The prediction results of BPNN and GA-BPNN were compared, and the prediction results showed that the prediction performance of GA-BPNN was much better. Furthermore, a screening test experiment was conducted using GA-BPNN for multiple classes of meteorological parameters, and a smaller number of parameter sets were identified to simplify the prediction inputs. The values of running time, root mean square error, and mean absolute error of GA-BPNN are better than those of BPNN through the calculation and analysis of evaluation metrics. This study will contribute to a certain extent to improve the accuracy and efficiency of temperature prediction in the Laoshan landscape.

This paper investigated the stability of a class of neutral-type stochastic delayed neural networks with Markov switching. Under a general decay rate and weaker conditions on the neutral term, sufficient conditions for stability in the p-th moment, almost sure stability, and actual stability were established by constructing appropriate Lyapunov functions and applying the nonnegative semimartingale convergence theorem. The theoretical analysis was validated via MATLAB simulations using the Euler-Maruyama method.