人工神经网络-遗传算法优化酶法提取绿豆渣水溶性纤维素工艺研究

探讨了人工神经网络和遗传算法在酶法提取绿豆渣水溶性纤维素工艺优化方面的应用。基于RBF神经网络建立绿豆中的水溶性纤维素提取率预测模型,并利用遗传算法对影响提取率的主要因素进行寻优。由实际数据得出仿真结果,并通过验证实验表明该方法有效。     

基于神经网络遗传算法优化棉粕挤压膨化脱酚工艺

本文应用人工神经网络模拟了棉粕的挤压膨化脱酚工艺,建立了一个3层网络结构的BP神经网络模型用以预测游离棉酚的降解规律,采用十折交叉验证表明:选择隐藏层神经元数为8、网络训练函数为\     

基于人工神经网络和遗传算法的黑木耳糙米醋发酵条件优化

以黑木耳糙米为原料,研究醋酸发酵的工艺条件,对接种量、摇床转速和装料最参数进行了单因素和正交试验,摸索出参数优化范围,并利用人工神经网络和遗传算法联合对发酵条件进行优化,结果表明,当接种量为8.1%,摇床转速为169r/min,装料量为250mL三角瓶装料61mL时,醋酸含量可达3.855g/100mL,该方法与正交试验设计得出的结果相比,发酵酸度上升了8.0%,为发酵优化控制提供了一种更加准确的方法.     

遗传算法结合反向传播算法神经网络优化党参多糖的提取工艺

目的 通过遗传算法结合BP(Back Propagation)神经网络,与正交试验结果作对比,优化党参中粗多糖的提取工艺。方法 以党参多糖的提取得率为指标,采用三因素(提取次数、提取时间、料液比)优化BP神经网络模型参数,并建立网络模型,再利用遗传算法对网络进行目标寻优,获得党参多糖的最佳提取工艺。结果 得到的最优提取工艺为提取次数3次,提取时间2 h,加水量为10倍,在此条件下党参多糖得率预测值为55.29 mg/g,和实际测量值的相对误差仅为1.10% ,具有较好的网络预测性。本方法无需复杂的实验过程,就可以快速得到最优的提取条件,且所得的党参多糖提取率较正交试验最优工艺所得的党参多糖提取率高出5.42%。结论 利用遗传算法结合BP神经网络算法对党参提取粗多糖的提取工艺进行优化快速、高效、可行。     

Development of correlations and artificial neural network models to predict second-degree burn time for thermal-protective fabrics

Firefighters and operators working near industrial furnaces or ovens usually encounter extreme environmental conditions. Heat transfer through fabrics exposed to such high heat flux exposures causes skin burn. There are various parameters which govern heat transfer and second degree burn time in such situations. In this work, six dimensionless parameters associated with heat transfer through fabrics exposed to various flame and radiant heat flux exposures are obtained. Correlations for dimensionless second degree burn time are obtained in terms of other non-dimensional parameters. These separate correlations for each type of exposure can be used to obtain thermal protective performance (TPP) of any fabric exposed to different type of heat conditions, provided that fabric material, fabric geometric parameters, air gap between fabric and skin, and intensity of heat exposure are known. Artificial neural network (ANN) approach is also used to predict TPP of clothing. Multi-layer feed-forward back-propagation networks are developed for different type of exposures. Results shows that both correlations and ANN approach used in the study are promising. ANN model predictions are found to agree better with experimental observations as compared to the outcome of developed correlations. Importance of various dimensionless parameters obtained using dimensional analysis are also emphasized.     

壳聚糖-单糖美拉德反应产物的制备及其在抗菌和抗氧化中的应用研究进展

为了对美拉德反应体系进行综合评价以及优化茶氨酸-葡萄糖美拉德反应条件,本研究构建了茶氨酸-葡萄糖美拉德反应综合评价值的人工神经网络耦联遗传算法（BP-GA）模型,优化得到了最佳反应条件。先通过熵值法求出荧光值、A₂₉₄、A₄₂₀、葡萄糖及茶氨酸剩余量五个评价指标之间的权重,再结合单因素实验及正交试验结果,建立输入为实验因素参数,输出为美拉德反应综合评价值的BP-GA人工神经网络模型,最后使用建立好的模型得到茶氨酸-葡萄糖美拉德反应的最优条件及相应的美拉德反应综合评价值。结果表明,通过建立的BP-GA模型进行全局优化得到茶氨酸-葡萄糖美拉德反应的最优条件为：反应温度117.6 ℃,反应时间1.8 h,pH7.3,羰基氨比1:2,此条件下美拉德反应的综合评价值为93.22。经过与正交试验得到的结果比较,发现BP-GA模型具有良好的预测性能。利用BP-GA模型,本研究得到了模型美拉德反应的最优条件,这将为美拉德反应模拟体系的构建及反应的预测提供参考。

     

Optimisation of the addition of carrot dietary fibre to a dry fermented sausage (sobrassada) using artificial neural networks

An optimisation problem was formulated to maximise the amount of carrot dietary fibre (CDF) in a dry fermented sausage, while maintaining product quality, by using 0–12% CDF as the decision variable, and limiting values of several physico-chemical and textural parameters (moisture content, water activity, pH, colour, non-protein nitrogen, free fatty acid, compression work and hardness) as constraints.