CAJ | 학술논문

系统阐述了人工神经网络的基本原理，分析了巷道围岩稳定性分类中的指标选取及分类方法。目前的大部分研究在确定分类指标的基础上，将巷道围岩稳定性分为4种或5种类型，建立高精度模型判别围岩稳定性类型成为研究的关键点。从基于BP神经网络、RBF神经网络、与其他方法结合的神经网络巷道围岩稳定性分类三方面剖析了目前的研究现状。指出非线性动态系统、多方法联合应用、分类指标科学化是基于神经网络的巷道围岩稳定性分类的发展方向。
계통천술료인공신경망락적기본원리，분석료항도위암은정성분류중적지표선취급분류방법。목전적대부분연구재학정분류지표적기출상，장항도위암은정성분위4충혹5충류형，건립고정도모형판별위암은정성류형성위연구적관건점。종기우BP신경망락、RBF신경망락、여기타방법결합적신경망락항도위암은정성분류삼방면부석료목전적연구현상。지출비선성동태계통、다방법연합응용、분류지표과학화시기우신경망락적항도위암은정성분류적발전방향。
This paper systematically elaborated the basic principle of the artificial neural network and analyzed the index selection and classification methods in the stability classification of the roadway surrounding rock. Based on the classification indexes presently determined in most researches, the stability of the roadway surrounding rock was classified into 4 or 5 types, building a high-accuracy model to determine the stability type of surrounding rock has become a key point in research. The paper also dissected the current research status from three stability classifications of roadway surrounding rock based on BP neural network, RBF neural network and the neural network combined with other methods. The paper pointed out that the nonlinear dynamic system, the combined application of many methods and the scientific classification indexes were the development directions of the stability classification of the roadway surrounding rock based on neural network.