A differential-equation-based training rule with distributed PID control is proposed for a symmetric Wuxing neural network and tested on MNIST.
From a mathematical perspective, we propose the use of differential equations in place of chain derivation to address the one -to-one correspondence between parameters and outcomes
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A Neural Network Training Method Based on Distributed PID Control
A differential-equation-based training rule with distributed PID control is proposed for a symmetric Wuxing neural network and tested on MNIST.