论文标题
小波神经网络与基于小波的神经网络
Wavelet Neural Networks versus Wavelet-based Neural Networks
论文作者
论文摘要
这是一系列研究中的第一篇论文，我们介绍了一种新型的神经网络（NNS） - 基于小波的神经网络（WBNNS），并研究了它们的特性和应用的潜力。我们开始研究与当前现有类型的小波神经网络（WNN）的比较，并表明WBNNS的表现非常好。 WBNNS的广泛优势的原因之一是它们基于生物三对多分析分析（MRA）的高级分层树结构。造成这种情况的另一个原因是我们的新想法实施将小波树深度纳入NN的神经宽度。小波深度和神经深度的角色的分离在概念和算法上简单但高效的方法提供了一种迅速增加群和深WBNNS功能的方法，以及机器学习过程的快速加速。
This is the first paper in a sequence of studies in which we introduce a new type of neural networks (NNs) -- wavelet-based neural networks (WBNNs) -- and study their properties and potential for applications. We begin this study with a comparison to the currently existing type of wavelet neural networks (WNNs) and show that WBNNs vastly outperform WNNs. One reason for the vast superiority of WBNNs is their advanced hierarchical tree structure based on biorthonormal multiresolution analysis (MRA). Another reason for this is the implementation of our new idea to incorporate the wavelet tree depth into the neural width of the NN. The separation of the roles of wavelet depth and neural depth provides a conceptually and algorithmically simple but highly efficient methodology for sharp increase in functionality of swarm and deep WBNNs and rapid acceleration of the machine learning process.
