Signaling in the control charts is usually followed by a substantial amount of delay, in which precise identification of the time when a change has occurred in a process simplifies the removal of change causes. This problem is referred to as change point (CP) estimation in the literature. This paper proposes a novel ensemble model to estimate CP under different processes’ changes in the phase II applications, known as ANNCP. It uses an evolutionary artificial neural network (ANN) as an underlying reasoning scheme to combine the predictions of multiple techniques and make the final decision. Specifically, a hybrid model of genetic algorithm (GA) and simulated annealing (SAN) with a new loss function is used to optimize the weights of ANN. The experimental results indicate that ANNCP can produce promising results under different conditions as compared with other state-of-the-art methods reported in the literature.

控制图中的信号发送通常伴随着大量的延迟，其中精确识别过程中发生变化的时间简化了去除变化原因的过程。这个问题在文献中被称为变化点 (CP) 估计。本文提出了一种新的集成模型来估计第二阶段应用中不同过程变化下的 CP，称为 ANNCP。它使用进化人工神经网络 (ANN) 作为底层推理方案，结合多种技术的预测并做出最终决策。具体而言，使用具有新损失函数的遗传算法（GA）和模拟退火（SAN）的混合模型来优化ANN的权重。