A Polynomial Neural network with Controllable Precision and Human-Readable Topology II: Accelerated Approach Based on Expanded Layer

• URL: http://arxiv.org/abs/2006.02901v1
• Date: Thu, 4 Jun 2020 17:56:24 GMT
• Title: A Polynomial Neural network with Controllable Precision and Human-Readable Topology II: Accelerated Approach Based on Expanded Layer
• Authors: Gang Liu and Jing Wang
• Abstract summary: Controllable and readable neural network (Gang-PNN) is the Taylor expansion in the form of network. We presented an accelerated method based on an expanded order to optimize CR-PNN.
• Score: 6.2611437040083855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How about converting Taylor series to a network to solve the black-box nature of Neural Networks? Controllable and readable polynomial neural network (Gang transform or CR-PNN) is the Taylor expansion in the form of network, which is about ten times more efficient than typical BPNN for forward-propagation. Additionally, we can control the approximation precision and explain the internal structure of the network; thus, it is used for prediction and system identification. However, as the network depth increases, the computational complexity increases. Here, we presented an accelerated method based on an expanded order to optimize CR-PNN. The running speed of the structure of CR-PNN II is significantly higher than CR-PNN I under preserving the properties of CR-PNN I.

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