CAJ | 학술논문

文章提出了一种基于遗传算法(GA)优化径向基函数(RBF)神经网络的焦炭质量预测模型。RBF网络存在两个关键问题：一是如何确定隐含层中心，而是如何调整网络权值。本文通过减聚类算法确定RBF网络基函数的中心数目，应用遗传算法对RBF网络权值进行优化。主要对焦炭的抗碎强度、耐磨强度、反应性指数和反应后强度使用GA优化RBF神经网络预测。结果表明该模型有较强适应性，同时能保证较高的预测精度，具有一定的实用价值。
문장제출료일충기우유전산법(GA)우화경향기함수(RBF)신경망락적초탄질량예측모형。RBF망락존재량개관건문제：일시여하학정은함층중심，이시여하조정망락권치。본문통과감취류산법학정RBF망락기함수적중심수목，응용유전산법대RBF망락권치진행우화。주요대초탄적항쇄강도、내마강도、반응성지수화반응후강도사용GA우화RBF신경망락예측。결과표명해모형유교강괄응성，동시능보증교고적예측정도，구유일정적실용개치。
An optimization of coke quality prediction model of radial basis function (RBF) neural networks based on genetic algorithms (GA) ispresented. RBF networks exists two key questions: First, how to determine the center of the hidden layer, and how to adjust the network weights. The number of center-based RBF network function is determined by reducing clustering algorithm, and genetic algorithm optimizes the RBF network weights. Coke crushing strength, the strength of GA by optimize RBF neural network prediction after abrasion resistance, reactivity index and response are studied. Simulation results show that the proposed model has strong adaptability and ensurs a high prediction accuracy, which has some practical value.