基于改进教学算法优化BP神经网络的催化剂碳含量预测模型

Predictive model for catalyst carbon content based on MTLBO-BP

为提高待生催化剂碳含量预测的准确性,提出一种基于改进的教学算法(MTLBO)来优化BP神经网络的预测模型.针对基础教学算法全局搜索能力差的问题,在教师阶段前后增加了预习和复习过程,并在学生阶段采用量子方式进行更新.测试结果表明,该改进能够提高教学算法全局探索和局部改良能力,利用改进教学算法可优化BP神经网络的权值和阈值,并进行待生催化剂碳含量预测.仿真结果表明,改进后预测模型的预测精度和泛化能力均有一定程度的提高.

To improve the prediction accuracy of the carbon content, a predictive model based on the improved BP neural network（BPNN） is presented by using the modified teaching-learning-based optimization（MTLBO） algorithm. In order to improve the global optimization ability of the basis teaching-learning-based optimization（TLBO） algorithm, a preview phase and a review phase are added in the MTLBO algorithm. The quantum-behaved learning strategy is adopted in the learning phase. Test results show that the MTLBO algorithm is valid to solve the global and local optimization problems. The MTLBO algorithm is used to optimize the weights and thresholds of the BPNN for the prediction of catalyst carbon content of the catalystic reforming unit. The simulation results show that the predictive precision and generalization ability of the proposed method have a certain degree of improvement.