Three-layer neural networks for spectral estimation

Wang, C.-J.; Wickert, M.A.; Wu, C.-H.

doi:10.1109/icassp.1990.115980

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…A three-layer neural network is a machine learning model that consists of an input layer, an intermediate layer, and an output layer [35]. Each layer contains neurons that are connected to each other.…”

Section: Three-layered Neural Network (Tlnn)mentioning

confidence: 99%

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

Tziotzios,

Pantazi,

Paraskevas

et al. 2024

Horticulturae

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The current study investigates the use of a non-destructive hyperspectral imaging approach for the evaluation of kiwifruit cv. “Hayward” internal quality, focusing on physiological traits such as soluble solid concentration (SSC), dry matter (DM), firmness, and tannins, widely used as quality attributes. Regression models, including partial least squares regression (PLSR), bagged trees (BTs), and three-layered neural network (TLNN), were employed for the estimation of the above-mentioned quality attributes. Experimental procedures involving the Specim IQ hyperspectral camera utilization and software were followed for data acquisition and analysis. The effectiveness of PLSR, bagged trees, and TLNN in predicting the firmness, SSC, DM, and tannins of kiwifruit was assessed via statistical metrics, including R squared (R²) values and the root mean square error (RMSE). The obtained results indicate varying degrees of efficiency for each model in predicting kiwifruit quality parameters. The study concludes that machine learning algorithms, especially neural networks, offer substantial accuracy, surpassing traditional methods for evaluating kiwifruit quality traits. Overall, the current study highlights the potential of such non-destructive techniques in revolutionizing quality assessment during postharvest by yielding rapid and reliable predictions regarding the critical quality attributes of fruits.

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Section: Three-layered Neural Network (Tlnn)mentioning

confidence: 99%

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits

Tziotzios,

Pantazi,

Paraskevas

et al. 2024

Horticulturae

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“…where A4 is the number of output nodes and (4) where tP,,, is the target output for the m-th neuron with the p-th input pattern, and yp,m is the output of the m-th neuron with the p-th input pattern.…”

Section: Feed-forward With Bp Algorithmsmentioning

confidence: 99%

“…Another intriguing use of neural networks is spectral estimation [4]. A three-layered neural network is trained to do the frequency transformation.…”

Section: Introductionmentioning

confidence: 99%

Neural networks in frequency bin detection

Hsieh

Wood

Shetty³

Proceedings of WESCON '93

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This paper studies the application of neural networks in frequency bin detection. The structure of the neural network, the initial condition of weights, and the creation of the training patterns for this specific application determine the dimension and the convergence behavior of the neural network. After trained, the neural network can detect the presence of multiple tones from time sampled data, which may contain amplitude errors, phase errors, and additive noise. Remedies for errors are proposed via modifying weights or increasing the lmension of the neural network. Simulation results are given to support the analysis.

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