摘要
针对连续搅拌反应釜(CSTR)具有的多重稳态性质,提出使用多个相同拓扑结构的神经网络模块组成的集成神经网络对CSTR的状态进行预测的方法。对集成神经网络的所有网络模块使用多目标粒子群优化算法进行同步训练,使训练结果收敛于参数空间内最优的Pareto面。避免了单一神经网络训练收敛到某一最优点可能产生的过拟和的问题;解决了使用传统训练方法对集成神经网络的子网络进行独立训练时增加学习算法复杂度的问题。对CSTR浓度预测的测试结果证明集成神经网络比同等规模的单一神经网络更适用于CSTR的状态参数预测。
Neural network ensemble, which is made up of many neural network modules with same topological structure, is used to state parameter prediction, aiming at multi steady states of Continuous Stirred Tank Reactor ( CSTR). In neural network ensemble, modules are trained synchronously by using Multi-Objective Particle Swarm Optimization (MOPSO). The network parameters are trained to converge at the optimal Pareto Front in parameter space. It avoids over-fitting, which may be the result of parameters converge to a certain optimal point in parameter space, in single neural network training. It gets rid of the computational complexity exten- ding, which is brought by training sub-networks separately in neural network ensemble. The MOPSO trained neural network ensemble and the same size single neural network are used to prediction of concentration in CSTR. The results shows that the former one is more suitable for CSTR state parameter prediction.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2007年第4期433-436,共4页
Computers and Applied Chemistry
基金
国家863高技术研究计划(2002AA412510
2002AA412420)
关键词
集成神经网络
FALCON神经网络
多目标粒子群优化
连续搅拌反应釜
neural network ensemble, fuzzy adaptive learning control network, multi-objective particle swarm optimization, continuous stirred tank reactor