The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

中文翻译：

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一种用于等级估计的混合神经网络-模糊逻辑-遗传算法

在矿山项目中，品位估算是一个非常重要且费钱/耗时的阶段，由于矿床结构的复杂性，这对地质学家和采矿工程师来说是一个挑战。为了克服这个问题，最近采用了几种人工智能技术，如人工神经网络 (ANN) 和模糊逻辑 (FL)，具有各种架构和特性。但是，由于两种方法的限制，它们只能在特定情况下才能产生预期的结果。例如，FL 中的一个主要问题是构建隶属函数 (MF) 的难度。其他问题，例如体系结构和局部最小值，也可以在 ANN 设计中找到。因此，本文提出了一种新的等级估计方法。这种基于 ANN 和 FL 的方法称为“协同神经模糊推理系统”（CANFIS），它结合了 ANN 和 FL 两种方法。这两种人工智能方法的结合是通过智能系统的语言和数字能力实现的。为了提高该系统的性能，遗传算法（GA）——作为解决复杂优化问题的众所周知的技术——也被用来优化网络参数，包括学习率、网络动量和 MFs 的数量。每个输入。这些技术（ANN、自适应神经模糊推理系统或 ANFIS）与这种新方法 (CANFIS-GA) 的比较还通过位于伊朗东阿塞拜疆的 Sungun 铜矿床的案例研究进行。