基于遗传神经网络的微滤膜通量的预测

针对BP神经网络在寻优过程中容易陷入局部极小值的缺点，将遗传算法和BP神经网络相结合，构造了一种基于遗传算法的反向传播（GABP）神经网络。网络的训练分为两步：首先利用遗传算法群体寻优策略，采用遗传算法对网络权值和阈值进行全局搜索，保证其落入全局最优点的邻域;然后再用梯度法对网络权值进行细化训练以进一步减小误差，使其收敛于全局最优解或性能很好的近似最优解。网络训练时使用的数据是在不同操作条件 （温度、压力、浓度）下，用标准孔径为0.1 μm 的亲水聚偏氟乙烯微滤膜过滤牛血清白蛋白(BSA)溶液时得到的膜通量实验数据，用训练好的GABP神经网络对微滤膜过滤BSA的膜通量预测研究结果表明，与传统的BP算法相比，GABP神经网络算法改善了网络收敛速度以及膜通量预测的准确度。

Flux prediction of micro-filtration devices based on genetic neural network

Whereas the back propagation (BP) neural network may easily go to the local minimum value in the optimization process, a genetic algorithm based BP(GABP) neural network was constructed by combining the genetic algorithm（GA）with the BP neural network. The characteristic of searching the group optimization for GA can prevent the GABP neural network from converging in local optimal solution and ensure that it finds the global optimum or second-best solution with good performance.The training of the GABP neural network was finished in two steps.The GA was firstly used to make a thorough searching in the global space for the weights and thresholds of the neural network, which can ensure they fall into the neighborhood of global optimal solution.Then, in order to improve the convergence precision, the gradient method was used to finely train the network and find the global optimum or second-best solution with good performance.The experimental data of an orthogonal design (temperature, pressure, concentration) for a micro-filtration device (1 μm hydrophilic polyvinylidene fluoride micro-filtration membrane for bovine serum albumin filtration) were used as the sample data for training the GABP neural network, so the well-trained network can be used to predict the flux of the micro-filtration devices.The results showed that this method had greatly improved the convergence speed and the prediction accuracy of the traditional BP network.