This research focuses to propose a new hybrid approach which combined the recurrent fuzzy neural network (RFNN) with particle swarm optimization (PSO) algorithm to simulate the flyrock distance induced by mine blasting. Here, this combination is abbreviated using RFNN–PSO. To evaluate the acceptability of RFNN–PSO model, adaptive neuro-fuzzy inference system (ANFIS) and non-linear regression models were also used. To achieve the objective of this research, 72 sets of data were collected from Shur river dam region, in Iran. Maximum charge per delay, stemming, burden, and spacing were considered as input parameters in the models. Then, the performance of the RFNN–PSO model was evaluated against ANFIS and non-linear regression models. Correlation coefficient ( R 2 ), Nash and Sutcliffe (NS), mean absolute bias error (MABE), and root-mean-squared error (RMSE) were used as comparing statistical indicators for the assessment of the developed approach’s performance. Results show a satisfactory achievement between the actual and predicted flyrcok values by RFNN–PSO with R 2 , NS, MABE, and RMSE being 0.933, 0.921, 13.86, and 15.79, respectively.

中文翻译：

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基于RFNN结合PSO的爆破飞石智能模拟新方法

本研究的重点是提出一种新的混合方法，将递归模糊神经网络（RFNN）与粒子群优化（PSO）算法相结合，模拟矿山爆破引起的飞石距离。此处，此组合使用 RFNN-PSO 进行缩写。为了评估 RFNN-PSO 模型的可接受性，还使用了自适应神经模糊推理系统 (ANFIS) 和非线性回归模型。为实现本研究的目标，从伊朗舒尔河大坝地区收集了 72 组数据。每个延迟的最大电荷、词干、负担和间隔被视为模型中的输入参数。然后，针对 ANFIS 和非线性回归模型评估 RFNN-PSO 模型的性能。相关系数 (R 2 )，纳什和萨特克利夫 (NS)，平均绝对偏差误差 (MABE)，均方根误差 (RMSE) 被用作比较统计指标，用于评估所开发方法的性能。结果表明，RFNN-PSO 在实际和预测的 flyrcok 值之间取得了令人满意的结果，其中 R 2 、NS、MABE 和 RMSE 分别为 0.933、0.921、13.86 和 15.79。