Abstract This paper presents a new approach to building an interval model for an industrial process with uncertainty that employs an interval neural network (INN), which can solve problems such as model structure demands and complexity limitations in the conventional unknown but bounded (UBB) errors method. A new architecture for an interval random vector functional-link network (IRVFLN) and its learning algorithm with penalty factors are proposed, to solve the problems such as the local minima, slow convergence, and very poor sensitivity to learning rate settings in the interval feed-forward neural networks with error back-propagation (IBPNNs). As an application case study, the IRVFLN is used to model the glutamic acid fermentation process under the condition of bounded-error data, and the test results indicate that the accuracy of the IRVFLN model meets the manufacturing requirements. The comparison is performed with IBPNN, and the results demonstrate that the proposed network outperforms IBPNN both on effectiveness and efficiency. Also, a comparison is given with a crisp (point-valued) approach using RVFLN, and the results show that the crisp approach is less reliable when existing uncertainties in measuring or process.

中文翻译：

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用区间随机向量功能链接网络对不确定过程进行建模

摘要 本文提出了一种使用区间神经网络（INN）为不确定性工业过程建立区间模型的新方法，该方法可以解决传统未知但有界（UBB）误差中的模型结构需求和复杂性限制等问题。方法。提出了一种新的区间随机向量功能链路网络（IRVFLN）架构及其带惩罚因子的学习算法，以解决区间馈送中局部极小值、收敛速度慢、对学习率设置敏感性极差等问题。 -带有误差反向传播的前向神经网络 (IBPNNs)。作为应用案例研究，IRVFLN 用于在有界误差数据条件下对谷氨酸发酵过程进行建模，测试结果表明，IRVFLN模型的精度满足制造要求。与 IBPNN 进行比较，结果表明所提出的网络在有效性和效率上均优于 IBPNN。此外，还与使用 RVFLN 的清晰（点值）方法进行了比较，结果表明，当测量或过程中存在不确定性时，清晰方法不太可靠。