Comparison of Back Propagation Neural Network and Genetic Algorithm Neural Network for Stream Flow Prediction

Gowda, C. Chandre; Mayya, S. G.

doi:10.1155/2014/290127

“…However, backpropagation has the problem of falling into a local optimal solution. Some scholars use evolutionary algorithms to replace the backpropagation method and compare its effectiveness [43][44][45]. They indicate that evolutionary algorithms can not only solve the problem of limited optimization effect and local convergence but also improve the accuracy of prediction.…”

Section: B Tool Wear Prediction Resultsmentioning

confidence: 99%

Tool wear prediction using a hybrid of tool chip image and evolutionary fuzzy neural network

Lin

¹

,

Jhang

²

,

Chen

³

2021

Int J Adv Manuf Technol

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This paper proposed an evolutionary fuzzy neural network (EFNN) for tool wear prediction. The material chip is affected by cutting conditions during the cutting process. The different tool wear status causes different chip color which means the color of material chip can be an important factor for tool wear prediction. In this study, an industrial camera is used to capture chip image and convert it into CIE xy chromaticity features through a color conversion model. In addition, to improve the prediction accuracy, a dynamic group cooperative particle swarm optimization (DGCPSO) is proposed to optimize the EFNN parameters. The cutting time and CIE xy value are used as the input of the EFNN, and the output is predicted tool wear value. The experimental results show that the mean absolute percentage error (MAPE) of the proposed EFNN is 2.83% better than other methods.

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“…However, backpropagation has the problem of falling into a local optimal solution. Some scholars use evolutionary algorithms to replace the backpropagation method and compare its effectiveness [43][44][45]. They indicate that evolutionary algorithms can not only solve the problem of limited optimization effect and local convergence but also improve the accuracy of prediction.…”

Section: B Tool Wear Prediction Resultsmentioning

confidence: 99%

Tool Wear Prediction Using a Hybrid of Tool Chip Image and Evolutionary Fuzzy Neural Network

Lin

¹

,

Jhang

²

,

Chen

³

2021

Preprint

1

0

View full text Add to dashboard Cite

This paper proposed an evolutionary fuzzy neural network (EFNN) for tool wear prediction. The material chip is affected by cutting conditions during the cutting process. The different tool wear status causes different chip color which means the color of material chip can be an important factor for tool wear prediction. In this study, an industrial camera is used to capture chip image and convert it into CIE xy chromaticity features through a color conversion model. In addition, to improve the prediction accuracy, a dynamic group cooperative particle swarm optimization (DGCPSO) is proposed to optimize the EFNN parameters. The cutting time and CIE xy value are used as the input of the EFNN, and the output is predicted tool wear value. The experimental results show that the mean absolute percentage error (MAPE) of the proposed EFNN is 2.83% better than other methods.

show abstract

“…Several models are applied and tested with various combinations of layers (i.e., input layer, hidden layer, and output layer) and four activation functions (i.e., semilinear, sigmoid bipolar sigmoid, and the hyperbolic tangent function). Following Gowda and Mayya [48], the parameters of the ANN architecture in terms of learning rate, momentum, bias, the number of hidden neurons, and the activation constant were considered. Trial and error procedure was adopted to choose the optimal value of each structured parameter of network model.…”

Section: Multilayer Perception Architecturementioning

confidence: 99%

Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model

Ali¹,

Hussain

²

,

Faisal³

et al. 2017

Advances in Meteorology

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These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the country's environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation ( ), and Root Mean Square Error (RMSE)). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision-making.

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Comparison of Back Propagation Neural Network and Genetic Algorithm Neural Network for Stream Flow Prediction

Cited by 19 publications

References 11 publications

Tool wear prediction using a hybrid of tool chip image and evolutionary fuzzy neural network

Tool wear prediction using a hybrid of tool chip image and evolutionary fuzzy neural network

Tool Wear Prediction Using a Hybrid of Tool Chip Image and Evolutionary Fuzzy Neural Network

Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model

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