基于模拟退火的粒子群算法优化的二次回归在混凝土抗压强度预测中的应用

混凝土抗压强度与其影响因素之间存在着很强的非线性关系，精确预测混凝土抗压强度对提高工程质量和施工进度等具有重要意义。为了提高预测值的精确度，建立了二次回归预测模型，利用基于模拟退火的粒子群算法对模型系数进行了优化和求解。实例仿真表明这种经智能算法优化后的二次回归预测模型优于传统神经网络预测模型，预测精度得到了较大提高。

Quadratic Regression Based on Optimization Algorithm of Simulated Annealing Particle Group Theory Application in the Concrete Compressive Strength Prediction

There is strong no linear relationship between compressive strength of concrete and its influencing factors. Predicting the compressive strength of concrete accurately is important significance for improving the project quality and construction progress. To improve the accuracy of the predicted value, established quadratic regression model, using simulated annealing panicle group algorithm has been optimized for the model coefficients and solution. The simulation shows that the optimized quadratic regression model of intelligent algorithm is better than traditional neural network prediction model, prediction accuracy has been greatly improved.