基于混合粒子群算法的微波酯化反应模型的优化

化工论文编号:HG095  论文字数:9509,页数:28

目  录
中文摘要 I
英文摘要 II
目  录 III
1. 绪论 1
2. 支持向量机(Support Vector Machines) 3
3. 粒子群算法 7
3.1  基本粒子群算法 7
3.2  混合粒子群算法 9
3.2.1  混沌粒子群优化算法 9
3.2.2  基于模拟退火的粒子群优化算法 11
3.3  混沌粒子群算法性能测试 11
4. 模型的建立和优化 12
4.1 数据预处理 12
4.2 交叉验证 14
4.3 参数的选择 15
4.4 建立模型 15
5. 总结与展望 19
致  谢 20
参考文献 21
附录 MATLAB代码 22

 摘 要：粒子群优化(Particle Swarm Optimization，PSO)是一种高效的优化搜索算法，在函数优化、神经网络训练、模式分类、模糊系统控制等领域得到了广泛的应用。标准粒子群优化（Standard Particle Swarm Optimization）算法容易使模型陷入局部极值，而采用混沌粒子群算法 (CPSO)，即对全局极值采用自适应混沌优化策略，当出现早熟收敛时，对部分较优粒子采用混沌优化策略，摆脱了局部极值，得到全局最优。本文针对微波酯化反应过程表现出较强的非线性以及众多的影响因素，利用偏最小二乘支持向量机（partial Least Square, LS-SVM）基于实验数据对水杨酸乙酯的微波催化合成反应进行建模。针对该反应，利用混沌粒子群算法优化偏最小二乘支持向量机模型，模型的拟合误差平方和为0.066%，得到最优条件为：酸醇比0.14，功率402W，催化剂用量3.00mL，反应时间42min，对应的产率为80.11%。
 
 关键词：酯化；建模；支持向量机；混合粒子群

Hybrid Particle Swarm Optimization of Microwave esterification reaction model
 
Abstract: PSO is an efficient search algorithm,it has been widely used In function optimization, neural network training, pattern classification, fuzzy system control and other fields. Standard PSO algorithm is easy to make the model into a local minimum. Chaotic particle swarm optimization (CPSO), the global extreme adaptive chaos optimization strategy, when there is premature convergence, the optimum particle on the part of the chaos optimization strategy used, out of a local minimum, obtain the global optimum. Chaotic particle swarm optimization using partial least squares support vector machine model. This article papers for the esterification process of microwave showed a strong non-linear as well as numerous factors，Using partial least squares support vector machine based on experimental data on the Microwave Synthesis of Ethyl Salicylate reaction modeling。Model fitting error square is 0.066%, the optimal conditions: acid alcohol ratio of 0.14,the power of 402W, catalyst of 3.00 mL and reaction time of 42 min. The corresponding yield is 80.11%.

Keywords：Esterification; modeling; support vector machine; hybrid particle swarm