Relaxed conditions for convergence of batch BPAP for feedforward neural networks

Shao, Hongmei; Wang, Jian; Liu, Lijun; Xu, Dongpo; Bao, Wendi

doi:10.1016/j.neucom.2014.11.039

Cited by 16 publications

(3 citation statements)

References 19 publications

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“…The results show better convergence compared to existing work. Authors in [15] improved the batch BPAP algorithm through their proposed dynamic training rate with a penalty. The structure of the algorithm is 2:2:1, using the sigmoid as the activation function.…”

Section: Related Workmentioning

confidence: 99%

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Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training

Al_Duais¹,

Mohamad²

2017

JTIT

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Abstract-The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is presented, which trains with adynamic training rate. This technique was implemented with a sigmoid function. Several data sets were used as benchmarks for testing the effects of the created dynamic training rate that we created. All the experiments were performed on Matlab. From the experimental results, the DBBPLR algorithm provides superior performance in terms of training, faster training with higher accuracy compared to the BBP algorithm and existing works.Keywords-artificial neural network (ANN), batch back propagation algorithm, dynamic training rate, speed up training, accuracy training.

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Section: Related Workmentioning

confidence: 99%

“…It has been used successfully in neural network training with a multilayer feed-forward network [1], [2]. The BP algorithm led to a tremendous breakthrough in the application of multilayer perceptions [3].…”

Section: Introductionmentioning

confidence: 99%

Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training

Al_Duais¹,

Mohamad²

2017

JTIT

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show abstract

“…Therefore, the scaling-proof classifier should not be too complicated and resource-intensive like deep neural networks. According to the universal approximation theorem for feedforward neural networks [13], [14], two-layer perceptron (2LP) could be applied to classify scaled objects. Advantages of 2LP are simplicity and high speed of classification [12], [15], [16].…”

Section: Related Workmentioning

confidence: 99%

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

Romanuke

2015

Information Technology and Management Science

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-The research is focused on optimising two-layer perceptron for generalised scaled object classification problem. The optimisation criterion is minimisation of inaccuracy. The inaccuracy depends on training parameters and hidden layer neuron number. After its statistics is accumulated, minimisation is executed by a numerical search. Perceptron is optimised additionally by extra training. As it is done, the classification error percentage does not exceed 3 % in case of the worst scale distortion.

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A self-organizing deep belief network for nonlinear system modeling

Qiao

Wang

et al. 2018

Applied Soft Computing

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Relaxed conditions for convergence of batch BPAP for feedforward neural networks

Cited by 16 publications

References 19 publications

Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training

Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

A self-organizing deep belief network for nonlinear system modeling

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