Study on an adaptive GMDH-PID controller using adaptive moment estimation

Wakitani, Shin; Yamamoto, Toru; Ishimura, Akihiro

doi:10.1109/smc.2017.8122841

Cited by 3 publications

(1 citation statement)

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“…DC -ADAM can get a more accurate gradients and faster in training progress, and easy to implement with minimal memory requirements. This research [2] on online learning algorithm by using Group Method Of Data Handling Based Proportional -Integral -Derivative (GMDH -PID) for non-linear systems. With online setting method using GMDH -PID using Adaptive Estimation Moments (ADAM), which is one method of optimization that can adapt to changes in the system and also can optimize many parameters with a low calculation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm

Singarimbun

2020

Data Science: J. of Computing and Appl. Informatics

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Back - propagation Neural Network has weaknesses such as errors of gradient descent training slowly of error function, training time is too long and is easy to fall into local optimum. Back - propagation algorithm is one of the artificial neural network training algorithm that has weaknesses such as the convergence of long, over-fitting and easy to get stuck in local optima. Back - propagation is used to minimize errors in each iteration. This paper investigates and evaluates the performance of Adaptive Moment Estimation (ADAM) to minimize the squared error in back - propagation gradient descent algorithm. Adaptive Estimation moment can speed up the training and achieve the level of acceleration to get linear. ADAM can adapt to changes in the system, and can optimize many parameters with a low calculation. The results of the study indicate that the performance of adaptive moment estimation can minimize the squared error in the output of neural networks.

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Section: Introductionmentioning

confidence: 99%