BP neural network prediction of the mechanical properties of porous NiTi shape memory alloy prepared by thermal explosion reaction

Li, Qiang; Yu, Jingyuan; Mu, Bai-Chun; Sun, Xudong

doi:10.1016/j.msea.2005.12.027

Cited by 72 publications

(31 citation statements)

References 9 publications

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“…The BP network is widely used in prediction, such as the prediction of the mechanical properties of porous NiTi shape memory alloy prepared by thermal explosion reaction [5] and the prediction of the processing parameters of liquid extrusion [6].…”

Section: ) Station Heat Map: This Module Ultilize Bicycle Datamentioning

confidence: 99%

GreenBicycling: A Smartphone-Based Public Bicycle Sharing System for Healthy Life

Zhao

Chen

Teng

et al. 2013

2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical A

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Gas-consumed urban transportation causes the problems of environment pollution and traffic congestion.Bicycling is an alternative transportation way for short-distance movement, which is a green and healthy style. Some modern cities are building Public Bicycle Sharing System (PBSS), so that inhabitants and tourists can temporarily use bicycles anywhere in the city. In this paper, GreenBicycling, a smartphone-based Public Bicycle Sharing System for healthy life is developed. GreenBicycling aims to improve the user experience and encourage cyclist to use BSS. It allows cyclist to query not only the current information of rental spots, but also the forecast state. To achieve this goal, an improved back propagation network prediction model is proposed. In addition, a quantitative measurement of calorie consumption in a riding trip is introduced into GreenBicycling, so that the cyclist could get intuitive understanding of how much calorie reduction from the riding. A simplified version of the GreenBicycling APP has been released in the Windows Phone Market.

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Section: ) Station Heat Map: This Module Ultilize Bicycle Datamentioning

confidence: 99%

GreenBicycling: A Smartphone-Based Public Bicycle Sharing System for Healthy Life

Zhao

Chen

Teng

et al. 2013

2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical A

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“…Due to its excellent ability of non-linear pattern recognition, generalization, self-organization and selflearning, the Artificial Neural Network Approach (ANNA) has been proved to be of widespread utility in engineering and is steadily advancing into diverse areas as material sciences (Li et al 2006), voice recognition, loan-risk assessment, stock market analysis, box office revenue forecasting (Zhang et al 2009) and military target discrimination. In geosciences and geo-engineering, neural networks have been applied in rock mechanics and rock engineering (Zhang et al 1991;Ghaboussi 1992;Lee and Sterling 1992), soil engineering (Kung et al 2007), well-log and well-test interpretation (Rogers et al 1992;AlKaabl and Lee 1993), seismic and satellite image processing (de Groot 1993;Penn et al 1993), groundwater characterization and remediation (Rizzo and Doughery 1994;Rogers and Dowla 1994), earthquake intensity prediction (Tung et al 1994), oil reservoir prediction (Yu et al 2008) and conductive fracture identification (Thomas and La Pointe 1995).…”

Section: Introductionmentioning

confidence: 99%

New Prediction Models for Mean Particle Size in Rock Blast Fragmentation

Kulatilake

Hudaverdi

2012

Geotech Geol Eng

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The paper refers the reader to a blast data base developed in a previous study. The data base consists of blast design parameters, explosive parameters, modulus of elasticity and in situ block size. A hierarchical cluster analysis was used to separate the blast data into two different groups of similarity based on the intact rock stiffness. The group memberships were confirmed by the discriminant analysis. A part of this blast data was used to train a single-hidden layer back propagation neural network model to predict mean particle size resulting from blast fragmentation for each of the obtained similarity groups. The mean particle size was considered to be a function of seven independent parameters. An extensive analysis was performed to estimate the optimum value for the number of units for the hidden layer for each of the obtained similarity groups. The blast data that were not used for training were used to validate the trained neural network models. For the same two similarity groups, multivariate regression models were also developed to predict mean particle size. Capability of the developed neural network models as well as multivariate regression models was determined by comparing predictions with measured mean particle size values and predictions based on one of the most applied fragmentation prediction models appearing in the blasting literature. Prediction capability of the trained neural network models as well as multivariate regression models was found to be strong and better than the existing most applied fragmentation prediction model. Diversity of the blasts data used is one of the most important aspects of the developed models.

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“…Because of its simplicity and its power to extract useful information from samples, the application of BP model is very wide recently (Li, Yu, Mu, & Sun, 2006). It allows specification of multiple input criterion, and generation of multiple output recommendations, and no assumption regarding the form of the functions relating input and output variables.…”

Section: Introductionmentioning

confidence: 99%