School of Management, Shanghai University of Engineering Science, Shanghai 201620, China.
School of International Trade and Economics, Shanghai Lixin University of Accounting and Finance, Shanghai 200030, China.
Comput Intell Neurosci. 2022 Apr 30;2022:2128370. doi: 10.1155/2022/2128370. eCollection 2022.
Scientific and accurate prediction of high-tech industries is of great practical significance for government departments to grasp the future economic operation and formulate development strategies. In this paper, aiming at some shortcomings of neural network (NN) applied in economic forecasting, GANN was introduced to construct the economic forecasting model of high-tech industry. Genetic algorithm (GA) has simple calculation and strong robustness and can generally ensure convergence to the global optimum, which effectively overcomes the shortcomings of NN using gradient descent method. In order to verify the feasibility of the economic forecasting model in this paper, the comparative experiments of different models are carried out in this paper. Experimental results show that the proposed algorithm has faster convergence speed and greater generalization ability, and the average error rate is reduced to about 1%. The prediction accuracy of this model reached 95.14%, which was about 11.93% higher than the previous model. Applying the economic forecasting model in this paper to the economic forecasting of high-tech industries can provide the means and reference value for the government to formulate regional future economic development plans, forecast, and control the economic growth and development direction.
科学准确地预测高科技产业对政府部门把握未来经济运行和制定发展战略具有重要的现实意义。针对神经网络（NN）在经济预测中存在的一些不足，本文引入广义回归神经网络（GANN）构建了高科技产业经济预测模型。遗传算法（GA）计算简单，鲁棒性强，能够保证全局最优收敛，有效克服了 NN 采用梯度下降法的缺点。为了验证本文经济预测模型的可行性，本文进行了不同模型的对比实验。实验结果表明，所提出的算法具有更快的收敛速度和更强的泛化能力，平均误差率降低到 1%左右。该模型的预测精度达到 95.14%，比之前的模型高出约 11.93%。将本文的经济预测模型应用于高科技产业的经济预测，可以为政府制定区域未来经济发展规划、预测和控制经济增长和发展方向提供手段和参考价值。
