Cascading model based back propagation neural network in enabling precise classification

Liu, Yang; Jing, Weizhe; Xu, Lixiong

doi:10.1109/fskd.2016.7603142

Cited by 5 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BPNN is a widely used machine learning method with excellent performance in digital regression. A standard BPNN usually consists of an input layer, a hidden layer, and an output layer [23]. As shown in Figure 11, the BPNN has two hidden layers and each layer has m units as an example.…”

Section: Backpropagation Neural Network (Bpnn)mentioning

confidence: 99%

Digital Model of Deflection of a Cable-Stayed Bridge Driven by Deep Learning and Big Data Optimized via PCA-LGBM

Xia¹,

Guo

Yue

et al. 2023

Sustainability

View full text Add to dashboard Cite

Based on big data, we can build a regression model between a temperature field and a temperature-induced deflection to provide a control group representing the service performance of bridges, which has a positive effect on the full life cycle maintenance of bridges. However, the spatial temperature information of a cable-stayed bridge is difficult to describe. To establish a regression model with high precision, the improved PCA-LGBM (principal component analysis and light gradient boosting machine) algorithm is proposed to extract the main temperature features that can reflect the spatial temperature information as accurately and efficiently as possible. Then, in this article, we searched for a suitable digital tool for modeling the regressive relationship between the temperature variables and the temperature-induced deflection of a cable-stayed bridge. The multiple linear regression model has relatively low precision. The precision of the backpropagation neural network (BPNN) model has been improved, but it is still unsatisfactory. The nested long short-term memory (NLSTM) model improves the nonlinear expression ability of the regression model and is more precise than BPNN models and the classical LSTM. The architecture of the NLSTM network is optimized for high precision and to avoid the waste of computational costs. Based on the four main temperature features extracted via the PCA-LGBM, the NLSTM network with double hidden layers and 256 hidden units in each hidden layer has much higher precision than the other regression models. For the NLSTM regression model of the temperature-induced deflection of a cable-stayed bridge, the mean absolute error is only 4.76 mm, and the mean square error is only 18.57 mm2. The control value of the NLSTM regression model is precise and thus provides the potential for early detection of bridge anomalies. This article can provide reference processes and a data extraction algorithm for deflection modeling of other cable-stayed bridges.

show abstract

Section: Backpropagation Neural Network (Bpnn)mentioning

confidence: 99%

Digital Model of Deflection of a Cable-Stayed Bridge Driven by Deep Learning and Big Data Optimized via PCA-LGBM

Xia¹,

Guo

Yue

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…DL Model (forward propagation) [18] is the very basis of learning in which features from the first layer is carried forward to the next layer. For training, a widely popular algorithm is Back-Propagation [19], in which gradients or relative difference between iterations of calculating weight functions are minimized by a backward-looking architecture as shown below. Back propagation is a mean-squared-error function which is differentiable.…”

Section: Fig 3 Roc Curve Of Logistic Regressionmentioning

confidence: 99%

A Cognitive Framework to Secure Smart Cities

et al. 2018

View full text Add to dashboard Cite

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms.

show abstract

A Case Study for Software Quality Evaluation Based on SCT Model With BP Neural Network

et al. 2020

View full text Add to dashboard Cite

With the increasing for function, scale, hierarchy and complexity of software project, the software life cycle and development stage show a trend of cross-cutting and fuzzy boundary. The non-technical factors, such as poor management and control during the implementation of software projects, are the major reason for causing the low success rate of software projects recently. Therefore, the software quality evaluation under complex environment should take the cross-influence between different stages of software life cycle and different quality evaluation standards into consideration. Our research is to construct a new software quality evaluation model by using the influence relationship and the influence intensity index between project management domain and project quality evaluation criteria including scope, cost, and time. First, we came up with the definition of software project management domain in the process of software project development and management. Second, we proposed a mathematical method for extracting the direct or indirect influence relation between them, and give a definition for the quantitative evaluation index and its calculation formula. At last we proposed to construct a neural network training model which includes evaluation model logic relationships and software quality quantitative evaluation index. Through study and training by simulated software project management data, we can discover some key data, such as normal threshold range of influence, factor weights, etc. Therefore, a complete evaluation system is built, and the scientific nature and accuracy of the proposal evaluation system will be improved. INDEX TERMS Software quality evaluation model, back propagation neural network, project management domain, project sub-management domain, SCT, CMMI.

show abstract

Cascading model based back propagation neural network in enabling precise classification

Cited by 5 publications

References 8 publications

Digital Model of Deflection of a Cable-Stayed Bridge Driven by Deep Learning and Big Data Optimized via PCA-LGBM

Digital Model of Deflection of a Cable-Stayed Bridge Driven by Deep Learning and Big Data Optimized via PCA-LGBM

A Cognitive Framework to Secure Smart Cities

A Case Study for Software Quality Evaluation Based on SCT Model With BP Neural Network

Contact Info

Product

Resources

About