DWNN:动态小波神经网络
动态小波神经网络(Dynamic Wavelet Neural Network,简称DWNN)是一种结合了小波变换和神经网络优势的先进计算模型。它通过引入动态机制,有效提升了信号处理和模式识别的能力。该模型在工程、金融分析、人工智能等多个综合领域都有广泛应用,因其缩写形式DWNN书写简便,在学术论文和技术文档中被频繁使用。
Dynamic Wavelet Neural Network具体释义
Dynamic Wavelet Neural Network的英文发音
例句
- In this paper, we present a dynamic wavelet neural network ( DWNN ) with a feedback and use it for nonlinear predictor to speech signal. Also, Its ability to learn function and superiority in appropriating the high dimensional function are analyzed.
- 提出一种带反馈单元的动态小波神经网络(DWNN)(DWNN)并将其用作语音信号的非线性预测器,分析了DWNN的函数学习能力和对高维函数学习的优越性。
- The dynamic wavelet neural network method is applied to predict the fatigue residual life of the composite material. The predicting result is very close to the testing result.
- 本文采用动态小波神经网络(DWNN)方法,对复合材料疲劳剩余寿命进行了预测,结果与试验结果很接近。
- Dynamic Wavelet Neural Network(DWNN) Method Applied to Predict Fatigue Residual Life of Composite Material
- 复合材料疲劳剩余寿命预测的动态小波神经网络(DWNN)方法
- Because of the inner memory of feedback unit, the dynamic wavelet neural network performs well in learning long-term dependences and can overcome the curse of dimension problem which often occurs in wavelet networks to some degree.
- 由于反馈单元的内部记忆能力,动态神经网络具有对长时相关的预测能力并能在一定程度上克服小波神经网络的维数灾难问题;
- Short-term Load Forecasting Model Based on Optimized Dynamic Recurrent Wavelet Neural Network
- 优化动态递归小波神经网络短期负荷预测模型
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