Evolving GMDH-neuro-fuzzy system with small number of tuning parameters

Bodyanskiy, Yevgeniy; Boiko, Olena; Zaychenko, Yuriy; Hamidov, Galib

doi:10.1109/fskd.2017.8392957

Cited by 2 publications

(1 citation statement)

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“…The paper [14] proposed GMDH-neuro-fuzzy system with small number of hyperparameters but with huge time complexity.…”

Section: Related Workmentioning

confidence: 99%

A Math Approach with Brief Cases towards Reducing Computational and Time Complexity in the Industrial Systems

Matviychuk¹,

Peráček²,

Shakhovska³

2020

Preprint

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The paper proposes a new principle of finding and removing elements of mathematical model, redundant in terms of parametric identification of the model. It allows reducing computational and time complexity of the applications built on the model. Especially this is important for AI based systems, systems based on IoT solutions, distributed systems etc. Besides, the complexity reduction allows increasing an accuracy of mathematical models implemented. Despite the model order reduction methods are well known, they are extremely depended however on the problem area. Thus, proposed reduction principles can be used in different areas, what is demonstrated in this paper. The proposed method for the reduction of mathematical models of dynamic systems allows also the assessment of the requirements for the parameters of the simulator elements to ensure the specified accuracy of dynamic similarity. Efficiency of the principle is shown on the ordinary differential equations and on the neural network model. The given examples demonstrate efficient normalizing properties of the reduction principle for the mathematical models in the form of neural networks.

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“…The paper [14] proposed GMDH-neuro-fuzzy system with small number of hyperparameters but with huge time complexity.…”

Section: Related Workmentioning

confidence: 99%