A complex exothermic batch reactor model was developed by using structure approaching hybrid neural networks(SAHNN).The optimal reactor temperature profiles were obtained via the PSO-SQP algorithm solving maximum prod...A complex exothermic batch reactor model was developed by using structure approaching hybrid neural networks(SAHNN).The optimal reactor temperature profiles were obtained via the PSO-SQP algorithm solving maximum product concentration problem based on recurrent neural network(RNN).Considering model-plant mismatches and unmeasured disturbances,a novel extended integral square error index(EISE)was proposed,which introduced mismatches of model-plant into the optimal control profile.The approach used a feedback channel for the control and therefore dramatically enhanced the robustness and anti-disturbance performance.The stability analysis of the one-step-ahead control strategy through SAHNN-based model was described based on Lyapunov theory in detail.The result fully demonstrated the effectiveness of the proposed optimal control profile.展开更多
含路径和终端约束的动态多目标优化是过程系统工程的一个重要研究方向,难度较高。传统蚁群算法仅适于离散问题,今采用混合正态分布描述信息素分布,并设计相应的解构造操作,使之拓宽至连续优化问题。通过对目标函数和约束矩阵的非劣排序...含路径和终端约束的动态多目标优化是过程系统工程的一个重要研究方向,难度较高。传统蚁群算法仅适于离散问题,今采用混合正态分布描述信息素分布,并设计相应的解构造操作,使之拓宽至连续优化问题。通过对目标函数和约束矩阵的非劣排序,确定解的等级,用以克服传统约束处理方法的局限性。借鉴了免疫系统的浓度概念,将其与解的等级结合,共同确定解的适应度,有助于保持种群的多样性。在更新信息素时将利用外部优解库和种群信息,可加快收敛速度。基于拥挤度距离更新外部优解库可更均匀地逼近Pareto最优解集。由此构建了一种基于免疫机制的多目标蚁群算法(Immune Mechanism based Multi-Objective Ant Colony Algorithm,IM-MOACA),并用于间歇反应器的动态多目标优化问题,效果良好,显示出较强的全局优化性能,能以较快的速度逼近真实的Pareto最优前沿,可为用户进行合理的决策分析提供有效的支持。展开更多
文摘A complex exothermic batch reactor model was developed by using structure approaching hybrid neural networks(SAHNN).The optimal reactor temperature profiles were obtained via the PSO-SQP algorithm solving maximum product concentration problem based on recurrent neural network(RNN).Considering model-plant mismatches and unmeasured disturbances,a novel extended integral square error index(EISE)was proposed,which introduced mismatches of model-plant into the optimal control profile.The approach used a feedback channel for the control and therefore dramatically enhanced the robustness and anti-disturbance performance.The stability analysis of the one-step-ahead control strategy through SAHNN-based model was described based on Lyapunov theory in detail.The result fully demonstrated the effectiveness of the proposed optimal control profile.
文摘含路径和终端约束的动态多目标优化是过程系统工程的一个重要研究方向,难度较高。传统蚁群算法仅适于离散问题,今采用混合正态分布描述信息素分布,并设计相应的解构造操作,使之拓宽至连续优化问题。通过对目标函数和约束矩阵的非劣排序,确定解的等级,用以克服传统约束处理方法的局限性。借鉴了免疫系统的浓度概念,将其与解的等级结合,共同确定解的适应度,有助于保持种群的多样性。在更新信息素时将利用外部优解库和种群信息,可加快收敛速度。基于拥挤度距离更新外部优解库可更均匀地逼近Pareto最优解集。由此构建了一种基于免疫机制的多目标蚁群算法(Immune Mechanism based Multi-Objective Ant Colony Algorithm,IM-MOACA),并用于间歇反应器的动态多目标优化问题,效果良好,显示出较强的全局优化性能,能以较快的速度逼近真实的Pareto最优前沿,可为用户进行合理的决策分析提供有效的支持。
