.png){kind=icon}
Return
Toward Deep Adaptive Hinging Hyperplanes
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2021.3079113.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The adaptive hinging hyperplane (AHH) model is a popular piecewise linear representation with a generalized tree structure and has been successfully applied in dynamic system identification. In this article, we aim to construct the deep AHH (DAHH) model to extend and generalize the networking of AHH model for high-dimensional problems. The network structure of DAHH is determined through a forward growth, in which the activity ratio is introduced to select effective neurons and no connecting weights are involved between the layers. Then, all neurons in the DAHH network can be flexibly connected to the output in a skip-layer format, and only the corresponding weights are the parameters to optimize. With such a network framework, the backpropagation algorithm can be implemented in DAHH to efficiently tackle large-scale problems and the gradient vanishing problem is not encountered in the training of DAHH. In fact, the optimization problem of DAHH can maintain convexity with convex loss in the output layer, which brings natural advantages in optimization. Different from the existing neural networks, DAHH is easier to interpret, where neurons are connected sparsely and analysis of variance (ANOVA) decomposition can be applied, facilitating to revealing the interactions between variables. A theoretical analysis toward universal approximation ability and explicit domain partitions are also derived. Numerical experiments verify the effectiveness of the proposed DAHH.
Keywords:
Neurons
Artificial neural networks
Network topology
Training
Topology
Optimization
Adaptive systems
Adaptive hinging hyperplanes (AHHs)
analysis of variance (ANOVA) decomposition
domain partition
piecewise linear (PWL)
skip-layer connection
Journal
IF:
8.9
Papers:
7.6K
Citations:
7.2W
Organization
Cited Papers
ShareSave
Regressor and structure selection in NARX models using a structured ANOVA approach.png)
AUTOMATICAOAAI
AUTOMATICAIF5.9
OAAIShareSaveShareSaveShareSaveShareSaveShareSave
View more