Work

Predictive modeling of complex mathematical functions using neural networks

Year: 2024

Type: article

Abstract: This research examines artificial neural networks' flexibility to forecast complicated computational processes. We created false data from the continuous field of polynomial, exponential, logarithmic, and trigonometric functions. Splitting all function training and testing sets created homogenous neural network models. MSE was compared to test data to evaluate models. Neural networks predict sine-cosine, exponential of sine, and cubic transformation functions accurately. Neural networks may capture complex operational relationships, as shown by an ordered comparison results table. This study shows that neural networks can solve mathematical modelling problems in automated forecasting.

Source: International Journal of Engineering & Technology

DOI: 10.14419/cz5tm443

Authors: Nadia Mahmood Hussien, Aysar Thamer Naser Tuaimah, Yasmin Makki Mohialden

Institution: Mustansiriyah University

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4407595747

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