Efficient mixed-signal synapse multipliers for multi-layer feed-forward neural networks

Youssefi, Bahar; Mirhassani, Mitra; Wu, Q. M. Jonathan

doi:10.1109/mwscas.2016.7870144

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The MFFNN model is similar to SFFNN with the exception that it has multiple hidden layers between input and output layers, [27]. Input signal moves to output through several hidden layers.…”

Section: The Mffnn Localisationmentioning

confidence: 99%

Determination of positioning accuracies by using fingerprint localisation and artificial neural networks

Koyuncu

2019

THERM SCI

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Fingerprint localisation technique is an effective positioning technique to determine the object locations by using radio signal strength, values in indoors. The technique is subject to big positioning errors due to challenging environmental conditions. In this paper, initially, a fingerprint localisation technique is deployed by using classical k-nearest neighborhood method to determine the unknown object locations. Additionally, several artificial neural networks, are employed, using fingerprint data, such as single-layer feed forward neural network, multi-layer feed forward neural network, multi-layer back propagation neural network, general regression neural network, and deep neural network to determine the same unknown object locations. Fingerprint database is built by received signal strength indicator measurement signatures across the grid locations. The construction and the adapted approach of different neural networks using the fingerprint data are described. The results of them are compared with the classical k-nearest neighborhood method and it was found that deep neural network was the best neural network technique providing the maximum positioning accuracies.

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Section: The Mffnn Localisationmentioning

confidence: 99%

Determination of positioning accuracies by using fingerprint localisation and artificial neural networks

Koyuncu

2019

THERM SCI

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Jaw fracture classification using meta heuristic firefly algorithm with multi-layered associative neural networks

Hashem

Hassanein

2018

Cluster Comput

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Efficient mixed-signal synapse multipliers for multi-layer feed-forward neural networks

Cited by 2 publications

References 14 publications

Determination of positioning accuracies by using fingerprint localisation and artificial neural networks

Determination of positioning accuracies by using fingerprint localisation and artificial neural networks

Jaw fracture classification using meta heuristic firefly algorithm with multi-layered associative neural networks

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