科技通报
科技通報
과기통보
Bulletin of Science and Technology
2015年
9期
192-195
,共4页
神经网络算法%投资银行%风险预测%动量因子%动态参数
神經網絡算法%投資銀行%風險預測%動量因子%動態參數
신경망락산법%투자은행%풍험예측%동량인자%동태삼수
neural network algorithm%investment banking%risk prediction%momentum factor%dynamic parameters
针对传统神经网络算法在投资银行风险预测的应用中表现出预测准确性不高的问题，提出了一种基于动态参数优化神经网络的投资银行风险预测模型。首先根据动态合并与删减规则，对参数进行自适应动态调整，得到最为合适的神经网络模型，然后为了加速收敛和防止振荡，引入一个动量因子，最后修改误差函数，在保证网络训练误差尽可能小的情况下使网络具有较小的权值。仿真试验结果表明，本文提出的基于动态参数优化神经网络的投资银行风险预测模型相比较标准神经网络算法，具有更高的预测准确性。
針對傳統神經網絡算法在投資銀行風險預測的應用中錶現齣預測準確性不高的問題，提齣瞭一種基于動態參數優化神經網絡的投資銀行風險預測模型。首先根據動態閤併與刪減規則，對參數進行自適應動態調整，得到最為閤適的神經網絡模型，然後為瞭加速收斂和防止振盪，引入一箇動量因子，最後脩改誤差函數，在保證網絡訓練誤差儘可能小的情況下使網絡具有較小的權值。倣真試驗結果錶明，本文提齣的基于動態參數優化神經網絡的投資銀行風險預測模型相比較標準神經網絡算法，具有更高的預測準確性。
침대전통신경망락산법재투자은행풍험예측적응용중표현출예측준학성불고적문제，제출료일충기우동태삼수우화신경망락적투자은행풍험예측모형。수선근거동태합병여산감규칙，대삼수진행자괄응동태조정，득도최위합괄적신경망락모형，연후위료가속수렴화방지진탕，인입일개동량인자，최후수개오차함수，재보증망락훈련오차진가능소적정황하사망락구유교소적권치。방진시험결과표명，본문제출적기우동태삼수우화신경망락적투자은행풍험예측모형상비교표준신경망락산법，구유경고적예측준학성。
Traditional neural network algorithm to predict the risk in the investment banking applications exhibit predictive accuracy is not high, risk prediction model proposed in this paper a dynamic parameter optimization of investment banking based on neural network, the first under the dynamic consolidation and deletion rules adaptive dynamic adjustment of parameters to obtain the most appropriate neural network model, then in order to accelerate convergence and prevent oscillation, the introduction of a momentum factor, last modified error function, to ensure the network training error as small as possible so that the network has a smaller case weights. The simulation results showed that the bank's risk prediction model based on neural network optimized dynamic parameters proposed investment compared to standard neural network algorithm, has higher prediction accuracy.
