当前位置: X-MOL 学术Eng. Comput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting
Engineering with Computers Pub Date : 2020-07-13 , DOI: 10.1007/s00366-020-01105-9
Mahdi Hasanipanah , Behrooz Keshtegar , Duc-Kien Thai , Nguyen-Thoi Troung

Blasting is the cheapest and most common method of rock excavation. The basic purpose of blasting is to breakage and displacement of rock mass and, on the other hand, it has some undesirable and inevitable effects such as flyrock. In this study, a novel hybrid artificial neural network (ANN) based on the adaptive musical inspired optimization method is proposed for accurate prediction of blast-induced flyrock. The dynamical adjusting process was adaptively introduced to enhance the ability of harmony search algorithm to obtain the optimum relationship between input variables, i.e., spacing, burden, stemming, powder factor and density of rock and output variable, i.e., flyrock. Two adjusting processes were used to update the new position of particles. The statistical information of the harmony memory was implemented in the proposed hybrid ANN coupled with adaptive dynamical harmony search (ANN-ADHS). The capacity for agreement, tendency, and accuracy of the proposed ANN-ADHS was compared with that of the ANN and two hybrid ANN models coupled by harmony search (ANN-HS) and particle swarm optimization (ANN-PSO) models using comparative statistics such as root mean square error (RMSE). The results confirmed viability and effectiveness of the ANN-ADHS model (with RMSE = 17.871 m and correlation coefficient (R2) = 0.929) and showed its capacity for better predictive performance compared to ANN-HS (with RMSE = 22.362 m and R2= 0.871), ANN-PSO (with RMSE = 24.286 m and R2= 0.832), and ANN (with RMSE = 24.319 m and R2= 0.831).

中文翻译:

一种近似于爆破飞石的自适应人工神经网络动态和谐搜索算法

爆破是最便宜和最常见的岩石开挖方法。爆破的基本目的是破坏和位移岩体,另一方面,它具有一些不良和不可避免的影响,如飞石。在这项研究中,提出了一种基于自适应音乐启发优化方法的新型混合人工神经网络 (ANN),用于准确预测爆炸诱发的飞石。自适应地引入动态调整过程,提高和声搜索算法获得输入变量,即间距、负荷、堵塞、粉体因子和岩石密度与输出变量即飞石之间的最佳关系的能力。两个调整过程用于更新粒子的新位置。和声记忆的统计信息在所提出的混合人工神经网络与自适应动态和声搜索(ANN-ADHS)相结合中实现。所提出的 ANN-ADHS 的一致性、趋势和准确性的能力与 ANN 和两个由和声搜索 (ANN-HS) 和粒子群优化 (ANN-PSO) 模型耦合的混合 ANN 模型使用比较统计数据进行了比较,例如作为均方根误差 (RMSE)。结果证实了 ANN-ADHS 模型(RMSE = 17.871 m 和相关系数 (R2) = 0.929)的可行性和有效性,并显示了与 ANN-HS(RMSE = 22.362 m 和 R2 = 0.871)相比具有更好的预测性能的能力)、ANN-PSO(RMSE = 24.286 m 且 R2= 0.832)和 ANN(RMSE = 24.319 m 且 R2= 0.831)。将所提出的 ANN-ADHS 的一致性、趋势和准确性的能力与 ANN 和通过和声搜索 (ANN-HS) 和粒子群优化 (ANN-PSO) 模型耦合的两个混合 ANN 模型进行比较作为均方根误差 (RMSE)。结果证实了 ANN-ADHS 模型(RMSE = 17.871 m 和相关系数 (R2) = 0.929)的可行性和有效性,并显示了与 ANN-HS(RMSE = 22.362 m 和 R2 = 0.871)相比具有更好的预测性能的能力)、ANN-PSO(RMSE = 24.286 m 且 R2= 0.832)和 ANN(RMSE = 24.319 m 且 R2= 0.831)。所提出的 ANN-ADHS 的一致性、趋势和准确性的能力与 ANN 和两个由和声搜索 (ANN-HS) 和粒子群优化 (ANN-PSO) 模型耦合的混合 ANN 模型使用比较统计数据进行了比较,例如作为均方根误差 (RMSE)。结果证实了 ANN-ADHS 模型(RMSE = 17.871 m 和相关系数 (R2) = 0.929)的可行性和有效性,并显示了与 ANN-HS(RMSE = 22.362 m 和 R2 = 0.871)相比具有更好的预测性能的能力)、ANN-PSO(RMSE = 24.286 m 且 R2= 0.832)和 ANN(RMSE = 24.319 m 且 R2= 0.831)。所提出的 ANN-ADHS 的准确性和准确性与 ANN 和两个混合 ANN 模型的和通过和声搜索 (ANN-HS) 和粒子群优化 (ANN-PSO) 耦合的模型进行了比较,使用比较统计数据,例如均方根误差 (RMSE) )。结果证实了 ANN-ADHS 模型(RMSE = 17.871 m 和相关系数 (R2) = 0.929)的可行性和有效性,并显示了与 ANN-HS(RMSE = 22.362 m 和 R2 = 0.871)相比具有更好的预测性能的能力)、ANN-PSO(RMSE = 24.286 m 且 R2= 0.832)和 ANN(RMSE = 24.319 m 且 R2= 0.831)。所提出的 ANN-ADHS 的准确性和准确性与 ANN 和两个混合 ANN 模型的和通过和声搜索 (ANN-HS) 和粒子群优化 (ANN-PSO) 耦合的模型进行了比较,使用比较统计数据,例如均方根误差 (RMSE) )。结果证实了 ANN-ADHS 模型(RMSE = 17.871 m 和相关系数 (R2) = 0.929)的可行性和有效性,并显示了与 ANN-HS(RMSE = 22.362 m 和 R2 = 0.871)相比具有更好的预测性能的能力)、ANN-PSO(RMSE = 24.286 m 且 R2= 0.832)和 ANN(RMSE = 24.319 m 且 R2= 0.831)。
更新日期:2020-07-13
down
wechat
bug