Supervised Artificial Neural Networks: Backpropagation Neural Networks

The problem of population is one of the problems in the Province of West Sumatra, especially in the City of Padang, Kota Bukitinggi, and the City of Payakumbuh which has a very fast population growth rate, this occurs due to several factors such as births, deaths, residents who come, and residents who leave. The highest population growth occurred in Padang City in 2018 amounting to 939,112 residents and the smallest population growth occurred in the City of Bukitinggi in 2014 amounting to 120,491 residents. The purpose of this study is to predict population growth that will occur in 2019 in the cities of Padang, Bukittinggi and Payakumbuh. The method used in this research is descriptive correlational by applying backpropagation neural networks. The application used is Matlab. Based on the problems and methods obtained, the predicted results in 2019 in Padang City amounted to 124,7150, Bukittinggi numbered 126,8040 and Payakumbuh totaled 128.7830. Keywords: Artificial Neural Networks, Backpropagation, Matlab.

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The Use of Fuzzy BackPropagation Neural Networks for the Early Diagnosis of Hypoxic Ischemic Encephalopathy in Newborns

Journal of Biomedicine and Biotechnology ◽

10.1155/2011/349490 ◽

2011 ◽

Vol 2011 ◽

pp. 1-5 ◽

Cited By ~ 3

Author(s):

Liu Li ◽

Huo Liqing ◽

Lu Hongru ◽

Zhang Feng ◽

Zheng Chongxun ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Early Diagnosis ◽

Recognition Rate ◽

Hypoxic Ischemic Encephalopathy ◽

Correct Recognition ◽

Backpropagation Neural Networks ◽

Artificial Neural ◽

Correct Recognition Rate ◽

Ischemic Encephalopathy

Objective. To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility.Methods. Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks.Results. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%.Conclusion. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE.

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Identifying Olive (Olea europaea) Cultivars Using Artificial Neural Networks

Journal of the American Society for Horticultural Science ◽

10.21273/jashs.124.5.527 ◽

1999 ◽

Vol 124 (5) ◽

pp. 527-531 ◽

Cited By ~ 11

Author(s):

S. Mancuso ◽

F.P. Nicese

Keyword(s):

Neural Networks ◽

Image Analysis ◽

Artificial Neural Networks ◽

Olea Europaea ◽

Mediterranean Basin ◽

Training Phase ◽

Backpropagation Neural Networks ◽

Artificial Neural ◽

The Mediterranean Basin ◽

Olea Europaéa

Backpropagation neural networks (BPNNs) were used to distinguish among 10 olive (Olea europaea L.) cultivars, originating throughout the Mediterranean basin. Identification was performed on the basis of 17 phyllometric parameters resulting from image analysis. Different BPNN architectures were attempted and best performance was achieved using a 17 × 20 × 10 BPNN. Networks were tested with sets of phyllometric parameters not involved in the training phase. Results enabled identification with certainty all cultivars tested.

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