The basics of multi-layer feedforward neural networks / Nurul Aityqah Yaccob and Farizuwana Akma Zulkifle

Yaccob, Nurul Aityqah and Zulkifle, Farizuwana Akma (2025) The basics of multi-layer feedforward neural networks / Nurul Aityqah Yaccob and Farizuwana Akma Zulkifle. Bulletin. Universiti Teknologi MARA, Negeri Sembilan.

Abstract

Artificial neural networks are computational models inspired by the brain, enabling them to capture complex nonlinear relationships between a response variable and its predictors. The simplest networks lack hidden layers, making them equivalent to linear regression models. Figure 1 illustrates a neural network representation of a linear regression model with four predictors. The coefficients assigned to these predictors are called "weights," and the forecasts are generated through a linear combination of the inputs. The weights are selected in the neural network framework using a "learning algorithm" that minimizes a "cost function," such as the mean squared error (MSE). However, for this simple case, 'near regression remains a more efficient approach.

Metadata

Item Type: Monograph (Bulletin)
Creators:
Creators
Email / ID Num.
Yaccob, Nurul Aityqah
UNSPECIFIED
Zulkifle, Farizuwana Akma
UNSPECIFIED
Subjects: L Education > L Education (General)
Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Universiti Teknologi MARA, Negeri Sembilan
Journal or Publication Title: What’s What PSPM
ISSN: 2756-7729
Keywords: Artificial, neural networks, computational, mean squared error, MSE
Date: 2025
URI: https://ir.uitm.edu.my/id/eprint/113822
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