Application of BP Neural Network for Predicting Anode Accuracy in ECM

Shang, Guang Qing; Sun, Chengnian

doi:10.1109/isise.2008.55

Cited by 15 publications

(10 citation statements)

References 11 publications

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“…In order to solve these problems, generally, data preprocessing before the training sample is very important and data normalization can make BP neural networks stronger. Given the descriptions of paper [35][36][37], adopt min-max normalization as the data normalization method. But there are the two categories of the economic indicators.…”

Section: Normalization Of Economic Evaluationmentioning

confidence: 99%

Design and analysis of a general data evaluation system based on social networks

Mao

et al. 2018

J Wireless Com Network

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A social network is a social structure with a set of social actors and other social interactions between actors. The social network supports a set of methods to explain the structure of whole social entities as well as a variety of theories in these structures. The study of these structures uses social network analysis to explain local and global patterns and examine network dynamics. The related economic data is important for our economic developments. We could analyze the economic data to explore the investments. In this paper, we focus on the design and analysis of automatic management systems to make a decision about the petroleum projects. Petroleum is the lifeblood of economic development. So, we need an automatic and effective management system to make the mining investments of petroleum. Before deciding to invest in the petroleum project, the engineers will describe this project by providing the sufficient economic data. Based on this data, the decision can undertake professional analysis, to determine whether the project is feasible. To automate the manual process and overcome the traditional evaluation method, the BP (back propagation) neural network is applied in a petroleum project economic evaluation. This method opens up a new way for the evaluation of petroleum projects. In this article, we first introduce the application of BP neural network. Then, we introduce the principle and shortcoming of BP neural network. Based on it, we optimize the BP neural network. Secondly, according to the characteristics of petroleum projects, we obtain the economic evaluation indicators about petroleum projects, which are used as the input of the BP neural network. Thirdly, we propose the design of the BP neural network and select the smallest simulation error of the BP neural network as a forecasting model of petroleum projects. In order to improve the accuracy, the labeled training samples are selected for BP neural network training. Finally, based on extensive simulation experimental results, we demonstrate our scheme about the BP neural network of petroleum project evaluation is effective and has overperformance than traditional schemes in terms of convenience and accurate decision-making suggestions.

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Section: Normalization Of Economic Evaluationmentioning

confidence: 99%

Design and analysis of a general data evaluation system based on social networks

Mao

et al. 2018

J Wireless Com Network

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“…[5]. Shanget .Q.P et la, presented the effect of different process parameters on anode accuracy and developed an ANN model with back propagation as the learning algorithm to predict the anode accuracy in electrochemical machining with an uneven inter electrode gap [6]. Kozak, j.et la, showed the concept and prototype of a computer aided engineering system that can be used to solve different tasks of ECM, such as: tool-electrode design, selection of optimal input machining parameters [7].…”

Section: Taguchi Integrated Least Square Support Vector Machine An Almentioning

confidence: 99%

Taguchi integrated Least Square Support Vector Machine an alternative to Artificial Neural Network analysis of electrochemical machining process

Nayak¹,

Tripathy²

2012

IOSRJMCE

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“…The learning process of BP network is actually divided into two periods: feed forward and back propagation [12]. The term 'feed forward' refers to a method by which a neural network processes the pattern and recalls patterns with learning samples, where as the term 'back propagation' describes how this type of neural network is trained.…”

Section: Bp Neural Networkmentioning

confidence: 99%

A PSO-BPNN-based model for energy saving in wireless sensor networks

Jia-jun

Guo

Chen

et al. 2009

2009 International Conference on Machine Learning and Cybernetics

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Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of transmissions of sensor nodes. In this paper, we propose an energy-efficient model based on improved BP neural network by particle swarm optimization (PSO-BPNN) in WSNs. The global optimized initial weights and threshold of BP network are obtained by PSO. And then PSO-BPNN is deployed at both the base station (BS) and the node in WSNs, helps to find out potential laws according to historical data sets. Only when the deviation between the actual and the predicted value at the node exceeds a certain threshold, the sampling value and new model are sent to BS. The experiments on ocean surface temperature 2008 made a satisfied performance. When the error threshold greater than 0.05 , it can decrease more than 80% data transmissions ℃ .

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Application of BP Neural Network for Predicting Anode Accuracy in ECM

Cited by 15 publications

References 11 publications

Design and analysis of a general data evaluation system based on social networks

Design and analysis of a general data evaluation system based on social networks

Taguchi integrated Least Square Support Vector Machine an alternative to Artificial Neural Network analysis of electrochemical machining process

A PSO-BPNN-based model for energy saving in wireless sensor networks

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