CAJ | 학술논문

神经树网络模型已成功应用于解决各类复杂的非线性问题,并且神经树网络模型的优化过程一般是先拓扑结构优化再参数优化,这种无参数信息的结构优化策略的缺点是干扰适应度的评价.鉴于此,提出一种改进的遗传规划(BGP)算法来综合神经树网络模型的两个优化过程.在两个时间序列预测问题上的仿真实验结果表明,所提出算法是一种有潜力且具备较好效能的方法.
신경수망락모형이성공응용우해결각류복잡적비선성문제,병차신경수망락모형적우화과정일반시선탁복결구우화재삼수우화,저충무삼수신식적결구우화책략적결점시간우괄응도적평개.감우차,제출일충개진적유전규화(BGP)산법래종합신경수망락모형적량개우화과정.재량개시간서렬예측문제상적방진실험결과표명,소제출산법시일충유잠력차구비교호효능적방법.
Neural tree network model has been successfully applied to solving a variety of complex nonlinear problems. The optimization of the neural tree model is divided into two steps in general: first structure optimization, and then parameter optimization. One major problem in the evolution of structure without parameter information is noisy fitness evaluation, so an improved breeder genetic programming algorithm is proposed to the synthesis of the optimization in neural tree network model. Simulation results on two time series prediction problems show that the proposed optimization strategy is a potential method with better performance and effectiveness.