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Evolutionary polymorphic neural networks in chemical engineering modeling.

机译：化工建模中的进化多态神经网络。

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Evolutionary Polymorphic Neural Network (EPNN) is a novel approach to modeling chemical, biochemical and physical processes. This approach has its basis in modern artificial intelligence, especially neural networks and evolutionary computing. EPNN can perform networked symbolic regressions for input-output data, while providing information about both the structure and complexity of a process during its own evolution.; In this work three different processes are modeled: (1) A dynamic neutralization process. (2) An aqueous two-phase system. (3) Reduction of a biodegradation model. In all three cases, EPNN shows better or at least equal performances over published data than traditional thermodynamics/transport or neural network models. Furthermore, in those cases where traditional modeling parameters are difficult to determine, EPNN can be used as an auxiliary tool to produce equivalent empirical formulae for the target process. (1) Feedback links in EPNN network can be formed through training (evolution) to perform multiple steps ahead predictions for dynamic nonlinear systems. (2) Unlike existing applications combining neural networks and genetic algorithms, symbolic formulae can be extracted from EPNN modeling results for further theoretical analysis and process optimization. (3) EPNN system can also be used for data prediction tuning. In which case, only a minimum number of initial system conditions need to be adjusted. Therefore, the network structure of EPNN is more flexible and adaptable than traditional neural networks. (4) Due to the polymorphic and evolutionary nature of the EPNN system, the initially randomized values of constants in EPNN networks will converge to the same or similar forms of functions in separate runs until the training process ends. The EPNN system is not sensitive to differences in initial values of the EPNN population. However, if there exists significant larger noise in one or more data sets in the whole data composition, the EPNN system will probably fail to converge to a satisfactory level of prediction on these data sets. (5) EPNN networks with a relatively small number of neurons can achieve similar or better performance than both traditional thermodynamic and neural network models.; The developed EPNN approach provides alternative methods for efficiently modeling complex, dynamic or steady-state chemical processes. EPNN is capable of producing symbolic empirical formulae for chemical processes, regardless of whether or not traditional thermodynamic models are available or can be applied. The EPNN approach does overcome some of the limitations of traditional thermodynamic/transport models and traditional neural network models.
机译：进化多态神经网络（EPNN）是一种对化学，生物化学和物理过程进行建模的新颖方法。这种方法在现代人工智能（尤其是神经网络和进化计算）中具有基础。 EPNN可以对输入-输出数据执行网络符号回归，同时提供有关流程在其自身演变过程中的结构和复杂性的信息。在这项工作中，对三个不同的过程进行了建模：（1）动态中和过程。 （2）水相两相系统。 （3）简化生物降解模型。在所有三种情况下，与传统的热力学/运输或神经网络模型相比，EPNN在已发布数据上表现出更好或至少相同的性能。此外，在传统建模参数难以确定的情况下，EPNN可以用作辅助工具来为目标过程生成等效的经验公式。 （1）可以通过训练（演化）来形成EPNN网络中的反馈链接，以对动态非线性系统进行多步提前预测。 （2）与结合神经网络和遗传算法的现有应用不同，可以从EPNN建模结果中提取符号公式，以进行进一步的理论分析和过程优化。 （3）EPNN系统也可用于数据预测调整。在这种情况下，仅需要调整最小数量的初始系统条件。因此，EPNN的网络结构比传统的神经网络更具灵活性和适应性。 （4）由于EPNN系统的多态性和进化性，EPNN网络中常量的初始随机值将在单独的运行中收敛到相同或相似形式的函数，直到训练过程结束。 EPNN系统对EPNN种群初始值的差异不敏感。但是，如果在整个数据组合中的一个或多个数据集中存在明显较大的噪声，则EPNN系统可能无法收敛到对这些数据集的令人满意的预测水平。 （5）与传统的热力学和神经网络模型相比，具有相对较少神经元的EPNN网络可以实现相似或更好的性能。先进的EPNN方法提供了有效建模复杂，动态或稳态化学过程的替代方法。 EPNN能够为化学过程生成符号经验公式，无论是否可以使用或可以应用传统的热力学模型。 EPNN方法确实克服了传统热力学/运输模型和传统神经网络模型的某些限制。

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