Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward artificial neural networks models are performed with derivative-based algorithms such as levenberg–marquardt and back-propagation learning algorithms in the first studies. In recent years, although many new heuristic algorithms have been proposed for different aims these heuristic algorithms are also frequently used in the training process of many different artificial neural network models. Pi-sigma artificial neural networks have different importance than many artificial neural network models with its higher-order network structure and superior forecasting performance. In this study, the training of Pi-Sigma artificial neural networks is performed by differential evolution algorithm uses DE/rand/1 mutation strategy. The performance of the proposed method is evaluated by two data sets and seen that the proposed method has a very effective performance compared with many artificial neural network models.

回顾人工神经网络的文献，大多数研究都是从前馈人工神经网络开始的，并且许多前馈人工神经网络模型的训练都是通过基于导数的算法（例如levenberg-marquardt和反向传播学习算法）来进行的。学习。近年来，尽管已经针对不同的目的提出了许多新的启发式算法，但是这些启发式算法也经常用于许多不同的人工神经网络模型的训练过程中。Pi-sigma人工神经网络具有较高的网络结构和优越的预测性能，因此与许多人工神经网络模型的重要性不同。在这项研究中，通过采用DE / rand / 1变异策略的差分进化算法对Pi-Sigma人工神经网络进行训练。通过两个数据集评估了该方法的性能，发现与许多人工神经网络模型相比，该方法具有非常有效的性能。