- Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi
- Cilt: 12 Sayı: 2
- Comparative Analysis of Neural Network Architectures for FFT Magnitude Spectrum Estimation
Comparative Analysis of Neural Network Architectures for FFT Magnitude Spectrum Estimation
Authors : Zeynep Kaya
Pages : 681-692
Doi:10.35193/bseufbd.1696439
View : 45 | Download : 157
Publication Date : 2025-11-30
Article Type : Research Paper
Abstract :This paper presents a comparative analysis of various neural network (NN) architectures for predicting the magnitude spectrum of the Fast Fourier Transform (FFT). To estimate the magnitude of the frequency components, feedforward neural network (FNN), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and one-dimensional convolutional neural network (1D-CNN) architectures were employed. The performance of each model is evaluated using mean squared error (MSE), mean absolute error (MAE), and R-square (R2) metrics. Experimental results show that the LSTM model outperforms the others, achieving an R-square value of 0.7742 and a MAE value of 0.1375. In contrast, the FNN model exhibits limited predictive capability on the Fourier-transformed data, which may be attributed to its insufficient capacity to model the temporal dependencies, and complex patterns present in the data.Keywords : FFT, LSTM, Büyüklük Spektrumu Tahmini, Sinir Ağı Mimarileri, Zaman Serisi Tahmini
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