This article contains a new subnet training method for modular neural networks, proposed with the inspiration of the principle of “an expert with other capabilities†. The key point of this method is that a subnet learns the neighbor data sets while fulfilling its main task: learning the objective data set. Additionally, a relative distance measure is proposed to replace the absolute distance measure used in the classical subnet learning method and its advantage in the general case is theoretically discussed. Both methodology and empirical study of this new method are presented. Two types of experiments respectively related with the approximation problem and the prediction problem in nonlinear dynamic systems are designed to verify the effectiveness of the proposed method. Compared with the classical subnet learning method, the average testing error of the proposed method is dramatically decreased and more stable. The superiority of the relative distance measure is also corroborated.

中文翻译：

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基于“具有其他能力的专家”的模块化神经网络方法学研究

本文包含一种新的模块化神经网络子网训练方法，该方法是在“具有其他能力的专家”的原则的启发下提出的。该方法的关键是子网在完成其主要任务：学习目标数据集的同时学习邻居数据集。此外，提出了一种相对距离度量来代替经典子网学习方法中使用的绝对距离度量，并从理论上讨论了它在一般情况下的优势。介绍了该新方法的方法论和实证研究。设计了分别与非线性动力系统中的逼近问题和预测问题有关的两种类型的实验，以验证该方法的有效性。与经典子网学习方法相比，所提方法的平均测试误差大大降低，更加稳定。相对距离测量的优越性也得到了证实。