Back-propagation algorithm with variable adaptive momentum

Hameed, Alaa Ali; Karlık, Bekir; Salman, Mohammad Shukri

doi:10.1016/j.knosys.2016.10.001

Cited by 89 publications

(51 citation statements)

References 29 publications

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“…The whole accuracy of all four classes were given in Table 3. Finally, we compared our LM method with other training methods, including back propagation (BP) [37], variable adaptive momentum BP (VAM-BP) [38], and genetic algorithm (GA) [39]. The comparison results were listed in Table 4 and Figure 4.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…ALGORITHM COMPARISON From Table 4, We can observe that BP [37] obtained an overall accuracy of 71.33± 1.48%, the VAM-BP [38] obtained an overall accuracy of 77.83± 1.31%, GA [39] obtained an overall accuracy of 73.67± 1.58%. Obviously, the proposed LM got the highest overall accuracy of 83.83± 2.92%.…”

Section: Approachmentioning

confidence: 91%

“…Overall Accuracy BP [37] 71.33± 1.48 VAM-BP [38] 77.83± 1.31 GA [39] 73.67± 1.58 LM (Proposed) 83.83± 2.92 FIGURE IV. ALGORITHM COMPARISON From Table 4, We can observe that BP [37] obtained an overall accuracy of 71.33± 1.48%, the VAM-BP [38] obtained an overall accuracy of 77.83± 1.31%, GA [39] obtained an overall accuracy of 73.67± 1.58%.…”

Section: Approachmentioning

confidence: 99%

See 2 more Smart Citations

Four-category Classification of Human Teeth Based on Wavelet Entropy and Back Propagation Neural Network

Jia¹,

Li²,

Quinn³

2018

Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)

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Abstract-The teeth are natural self-defense weapon of animals. For human, the pronunciation of language is closely related to the upper and lower front teeth (incisors). Furthermore, the cleanliness of the teeth even has an important influence on daily social activities and status of people. Therefore, when the teeth are diseased or need to be corrected, it becomes particularly vital to conduct a precise classification of different teeth in the oral cavity. In this paper, we will introduce our proposed method, back propagation neural network based on wavelet entropy and Levenberg-Marquardt algorithm, to make a correct classification of the teeth. The total accuracy of our method is 83.83± 2.92%. Our method is better than the state-of-art methods in performance.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Approachmentioning

confidence: 91%

Section: Approachmentioning

confidence: 99%

See 1 more Smart Citation

Four-category Classification of Human Teeth Based on Wavelet Entropy and Back Propagation Neural Network

Jia¹,

Li²,

Quinn³

2018

Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)

View full text Add to dashboard Cite

show abstract

“…With the development of the hardware, deep learning becomes a new favorite which attracts scholars not only from the computer industry but from other industries, including meteorological industry. BPNN is one of the most representatives in deep learning [42]. Once the data were put into the network, BPNN would optimize the parameters automatically.…”

Section: Perbpnnmentioning

confidence: 99%

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Tian¹,

Lei²,

Liu³

et al. 2020

J Cloud Comp

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Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

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“…As an algorithm of data classification, BP neural network can realize the classification of signal types [12][13]. The BP neural network is composed of input layer, hidden layer and output layer.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Study of Planetary Gear Fault Diagnosis Based on Energy of LMD and BP Neural Network

Li¹,

Chen²,

Liu³

2017

Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)

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Abstract-Planetary gear box has the characteristics of small volume and large transmission ratio and is widely used in construction machinery. After a long period of operation, the gear fault occurs frequently, which has a great influence on the equipment. However, due to its complex structure, the fault signal is often submerged in the inherent signal of the gearbox. In order to extract the fault feature from the signal, a method based on energy of Local mean decomposition (LMD) and Back Propagation (BP) neural network is proposed to solve this problem in this paper. Original signal is decomposed by LMD into 6 product functions (PF). The energy of each PF component are calculated and defined as the input of the BP neural network. Optimal model of neural network can be obtained based on sample training. The result of experimental shows that the proposed method can achieve an overall recognition rate of 95.5%, which proves that it is an effective method for planetary gear fault diagnosis.

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Back-propagation algorithm with variable adaptive momentum

Cited by 89 publications

References 29 publications

Four-category Classification of Human Teeth Based on Wavelet Entropy and Back Propagation Neural Network

Four-category Classification of Human Teeth Based on Wavelet Entropy and Back Propagation Neural Network

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Study of Planetary Gear Fault Diagnosis Based on Energy of LMD and BP Neural Network

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