论文部分内容阅读
针对小麦加工过程中的能耗问题,提出了一种基于广义回归神经网络(generalized regression neural networks,GRNN)的能效优化方法。为了解决GRNN的参数优化问题,采用一种基于改进的混沌粒子群优化算法的参数选择方法。并将此预测模型与混沌粒子群算法相结合,在面粉产量一定的前提下,寻找最优的加工参数组合,使得能耗最低。仿真结果表明,优化的GRNN模型具有较高的预测准确率,与混沌粒子群算法相结合,能够找到最优的加工参数组合,有效的进行小麦加工过程中的能效优化。
Aiming at the energy consumption in wheat processing, an energy efficiency optimization method based on generalized regression neural networks (GRNN) is proposed. In order to solve the parameter optimization problem of GRNN, a parameter selection method based on the improved chaotic particle swarm optimization algorithm is adopted. Combining this forecasting model with chaos particle swarm optimization, the optimal combination of processing parameters is found under the premise of a certain flour yield, which results in the lowest energy consumption. The simulation results show that the optimized GRNN model has a high prediction accuracy and can be combined with chaotic particle swarm optimization algorithm to find the optimal combination of processing parameters and effectively optimize the energy efficiency during wheat processing.