Application of a PSO-based neural network in analysis of outcomes of construction claims

Chau, Kwok Wing

doi:10.1016/j.autcon.2006.11.008

Cited by 242 publications

(105 citation statements)

References 19 publications

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“…Real-life applications of these soft computing techniques can be found in different fields (Chau, 2007;Wu and Chau, 2013;Taormina and Chau, 2015). In these studies, it was found that the ANN model performed better for short-interval prediction and produced similar results to classical time series models for long-interval forecast.…”

Section: Introductionsupporting

confidence: 48%

A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting

Duan

Huang

2016

JZUS-A

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Abstract:Accurate wave forecasting with a couple of hours of warning time offers improvements in safety for maritime operation-related activities. Autoregressive (AR) model is an efficient and highly adaptive approach for wave forecasting. However, it is based on linear and stationary theory and hence has limitations in forecasting nonlinear and non-stationary waves. Inspired by the capability of empirical mode decomposition (EMD) technique in handling nonlinear and non-stationary signals, this paper describes the development of a hybrid EMD-AR model for nonlinear and non-stationary wave forecasting. The EMD-AR model was developed by coupling an AR model with the EMD technique. Nonlinearity and non-stationarity were overcome by decomposing the wave time series into several simple components for which the AR model is suitable. The EMD-AR model was implemented using measured significant wave height data from the National Data Buoy Center, USA. Prediction results from various locations consistently show that the hybrid EMD-AR model is superior to the AR model. This demonstrates that the EMD technique is effective in processing nonlinear and non-stationary waves.

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Section: Introductionsupporting

confidence: 48%

A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting

Duan

Huang

2016

JZUS-A

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“…Currently, a model named XModel GUI tool is being developed for all above aims. As noted in [10][11][12][13][14][15], more work needs to be done in the future.…”

Section: Discussionmentioning

confidence: 99%

XML Specification for Complex Digital Logic Components

Chen¹

2013

TOAUTOCJ

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Abstract:Considering that the digital system design is a complex task, in order to meet some requirements such as effective performance, low costs and high reliability, and improve the development quality and efficiency, design activities need to be carried out under a precise description for completing the design cycle. This paper presents the XML specification method, and defines a subset of XML syntax for the complex digital logic component model. In this method, the logic mapping table is used to describe combinational logic components, state-oriented finite state machine to describe the behavior models of sequential logic components, and structure-oriented connector to describe structure models of composite hardware components.

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“…The most important benefit of PSO approach is its low computational cost and simple coding (38). Since PSO algorithm performed accurately to solve global optimum, it was applied to train the MLP in the current study.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

An Artificial Neural Network - Particle Swarm Optimization (ANN- PSO) Approach to Predict Heavy Metals Contamination in Groundwater Resources

Alizamir

Sobhanardakani

2018

Jundishapur J Health Sci

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Background: The quality of groundwater as the most important source for domestic, irrigation, and industrial purposes is affected by discharge of the chemicals from the anthropogenic resources. Therefore, the current study aimed at predicting heavy metals (As, Pb, Cu, and Zn) contamination in groundwater resources of Toyserkan Plain as an important agricultural area in Hamedan Province, West of Iran using artificial neural network -particle swarm optimization (ANN-PSO) approach. Methods: In the current study, samples were randomly selected from 20 groundwater wells with depth of 10 -90 m. The samples were filtered and kept cool in polyethylene bottles and then taken for the analysis of metal contents; they were acidified using nitric acid to reach pH < 2. Finally, element contents were determined using inductively coupled plasma -optical emission spectrometry (ICP-OES). Also, the performance of the PSO model was compared with that of ANN using Bayesian regulation (BR) training algorithm in terms of accuracy and model prediction efficiency.

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Application of a PSO-based neural network in analysis of outcomes of construction claims

Cited by 242 publications

References 19 publications

A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting

A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting

XML Specification for Complex Digital Logic Components

An Artificial Neural Network - Particle Swarm Optimization (ANN- PSO) Approach to Predict Heavy Metals Contamination in Groundwater Resources

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