一种自适应脊波网络模型

An adaptive ridgelet neural network model

将脊波理论和神经网络相结合,采用具有方向信息的脊波函数作为隐层神经元的激励函数,提出一种自适应脊波网络模型.由于脊波表征高维信息的稳定性和逼近线型奇异性的稀疏性,脊波网络能够以更小的网络规模逼近广泛的多变量函数类型.相对固定的脊波变换它具有更灵活的结构、快速并行的处理速度以及强容错性和鲁棒性.仿真结果也证明了其有效性.

By combining the ridgelet theory with the neual network, an adaptive rideglet neural network is presented by adopting a directional ridgelet function as the activation function of the hidden layer. For the stability of ridgelet in representing high dimensional data and the sparsity in approximating linear singularity （curvilinear singularity when using a multiscale ridgelet）, the proposed network can learn quit a large group of multivariate functions with a reduced scale. On the other hand, it has more flexible structure, rapider processing speed, greater tolerance and robustness than the fixed ridgelet transform. Simulation results are also included to prove its effciency.