Back Propagation Neural Network on the Forecasting System of Sea Food Material Demand

Lo, Chih-Yao

doi:10.1007/978-3-642-22456-0_22

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Neural Networks are always comparable to other prediction models when it comes to time series analysis. Back-propagating Neural Network model is used in [12] for forecasting the demand of sea food material demand. SVM is used for financial time series forecasting in [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Growth Curve Approach Using CGPANN for Predicting the Sustainability of New Food Products

Ali¹,

Khan²,

Mahmud³

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Part 7: Genetic AlgorithmsInternational audienceAn enhancement to the growth curve approach based on neuro evolution is proposed to develop various forecasting models to investigate the state and worth of the producer, to market a new product. The forecasting model is obtained using a newly introduced neuro evolutionary approach called Cartesian Genetic Programming based ANN (CGPANN). CGPANN helps in obtaining an optimum model for all the necessary parameters of an ANN. An accurate and computationally efficient model is obtained, achieving an accuracy as high as 93.37% on the time devised terrains, providing a general mechanism for forecasting models in mathematical agreement to its application in econometrics. Comparison with other contemporary model evidences the perfection of the proposed model thus its vital power in developing the growth curve approach for predicting the sustainability of new products

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Growth Curve Approach Using CGPANN for Predicting the Sustainability of New Food Products

Ali¹,

Khan²,

Mahmud³

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…The BPNN used gradient descent as optimization method in the training method, it detect and sum the loss function of gradient of all weights in the neural net, and the gradient is reused to update the weights for attempting to minimize the error function [8][9][10]. BPNN is classified as a supervised learning, thus, it need to supply required output to each data entry for computing the error function.…”

Section: Introductionmentioning

confidence: 99%

Application and evaluation of the neural network in gearbox

Al-Azzawi

2020

TELKOMNIKA

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We developed old designed of a Back-Propagation neural network (BPNN), which it was designed by other researchers, and we made modification in their structure. The 1 st velocity ratio was discriminated by lowest speed, and highest twist. The 6 th velocity ratio was discriminated by highest speed, and lowest twist. The aim of this paper is to design neural structure get best performance to control an electrical automotive transportation six-speed gearbox of the vehicle. We focus on the evaluation of the BPNN to select the suitable number of layers and neurons. Experimentally, the structure of the proposed BPNN are constructed from four layers: eight input nodes in the first layer that received data in binary number, 45 neurons in 1 st hidden-layer, 25 neurons in 2 nd hidden-layer, and 6 neurons in the fourth layer. The MSE and number of Epochs are the main factors used for the evaluation of the proposed structure, and compared with the other structures which was designed by other researchers. Experimentally, we discovered that the best value of Epoch and MSE was chosen when the BPNN consisted of two hidden-layers, 45, and 25 neurons in the 1 st and 2 nd hidden-layer respectively. The implementation was applied using MATLAB software.

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Back Propagation Neural Network on the Forecasting System of Sea Food Material Demand

Cited by 2 publications

References 7 publications

Enhancing Growth Curve Approach Using CGPANN for Predicting the Sustainability of New Food Products

Enhancing Growth Curve Approach Using CGPANN for Predicting the Sustainability of New Food Products

Application and evaluation of the neural network in gearbox

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