CAJ | 학술논문

运用数据挖掘技术对航材消耗的历史数据进行关联分析，筛选出对保障飞机飞行有重要作用的航材消耗数据，大大缩减了需要预测的航材数量，同时对消耗航材之间的内在影响关系进行量化。在分析人工鱼群算法原理的基础上，对算法中步长参数和视野范围参数的设置方法进行了改进。实例结果表明，运用小波神经网络预测航材消耗的方法大大降低了预测误差，说明了该方法的有效性、可行性和实用性。
운용수거알굴기술대항재소모적역사수거진행관련분석，사선출대보장비궤비행유중요작용적항재소모수거，대대축감료수요예측적항재수량，동시대소모항재지간적내재영향관계진행양화。재분석인공어군산법원리적기출상，대산법중보장삼수화시야범위삼수적설치방법진행료개진。실례결과표명，운용소파신경망락예측항재소모적방법대대강저료예측오차，설명료해방법적유효성、가행성화실용성。
In this paper, the correlation analysis on historical data of air material consumption was presented by using data mining technology, filting out the important material consumption data on the protection of aircraft flight, greatly reducing the amount of air material needed to forecast, and the influence between consumption materials relationship was quanti-fied. The principle of artificial fish swarm algorithm was analyzed, and the setting method of step parameter and visual field parameter was improved on the basis of it. The example results showed that the method of wavelet neural network could greatly reduce the prediction error of air material consumption, illustrated the effectiveness, feasibility and practicali-ty of the method.