This study contributes to developing a novel hybrid identification method based on intelligent algorithms, i.e. the leastsupportvectorregressionalgorithm (LS-SVR) and the artificial bee colony algorithm (ABC), to ...
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This study contributes to developing a novel hybrid identification method based on intelligent algorithms, i.e. the leastsupportvectorregressionalgorithm (LS-SVR) and the artificial bee colony algorithm (ABC), to deal with the identification of the simplified ship dynamic model while the outliers exist in the measurements. The ship dynamic model is directly derived from our previous work which has been well verified and validated. The outliers are detected by introducing the robust estimation method namely the 3 sigma principle and then deleted from the training data. The weighted version of LS-SVR (WLS-SVR) with spareness and robustness ability is used as the fundamental identification approach. To improve the performance of the WLS-SVR, the structural parameters involved in it are optimized by utilizing the artificial bee colony algorithm (ABC), and the weights of it are adaptively set with the use of the adaptive weight method. Two case studies including the simulation study on a container ship and the experimental study on an Unmanned Surface Vessel (USV) are carried out to test the proposed hybrid intelligent identification method. The simulation study demonstrates the effectiveness and the acceptable time complexity in terms of the engineering application of the proposed identification method through the comparison with the cross-validation method and particle swarm optimization algorithm optimized LS-SVR. In the experimental study, ABC-LSSVR, ABC-LSSVR with the 3 sigma principle (D-ABC-LSSVR), ABC-LSSVR with the adaptive weight (ABC-AWLSSVR), and ABC-LSSVR with both the 3 sigma principle and the adaptive weight (D-ABC-AWLSSVR) are applied to identify the steering model for the USV. The results indicate that the influence of the outliers on model identification is effectively diminished by the robust 3 sigma principle and the adaptive weight method and that the D-ABC-AWLSSVR outperforms over the other three identification methods in terms of the mean squar
Due to the nonlinear, non-stationery, complex and stochastic characteristics of short-term traffic flow time series, traditional prediction methods do not work well. This paper presents a short-term traffic flow forec...
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ISBN:
(纸本)9781479960798
Due to the nonlinear, non-stationery, complex and stochastic characteristics of short-term traffic flow time series, traditional prediction methods do not work well. This paper presents a short-term traffic flow forecasting model based on the leastsquaresupportvectorregression (LSSVR) algorithm, which is optimized by a glowworm swarm optimization (GSO) algorithm. The GSO algorithm is used to determine two core parameters in the learning process, which significantly influence the predicting performance in the model. An actual example of traffic flow data on one section of highway in Chengdu, China is used to evaluate the performance of the proposed LSS VR-GSO model. The experimental results show that the proposed LSSVR-GSO model has more accurate predicting results than the LSSVR model optimized by the genetic algorithm and the back-propagation neural network model.
Received signal strength (RSS) greatly differs due to the different occlusion directions and receiving device heterogeneity. It greatly affects the positioning accuracy. In this study, an adaptive indoor positioning m...
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Received signal strength (RSS) greatly differs due to the different occlusion directions and receiving device heterogeneity. It greatly affects the positioning accuracy. In this study, an adaptive indoor positioning method based on the direction discrimination and device conversion is proposed to solve these problems. This method is mainly composed of three parts: direction discrimination, device conversion and positioning models. First, the direction discrimination model can reduce the impact of a user's body occlusion. Best access points can be selected by principal component analysis to adapt to different directions and areas. Secondly, a device conversion model is used to reduce high offline work due to device heterogeneity. RSS of other devices can be converted to the value of one fixed device by leastsquares piecewise polynomial algorithm, without increasing the offline data collection workload. Finally, the results can be obtained by the positioning model. The problems of high dimensionality and non-linearity can be solved by the leastsquares supportvectorregressionalgorithm. Experimental results show that the proposed method can solve the problems of occlusion direction and device heterogeneity. The engineering applicability of positioning system can also be greatly improved.
A new combination method of beam-type finite element multiwavelet-based algorithm and leastsquaresupportvectorregression (LSSVR) algorithm is proposed for detecting the location and size of a crack in a pipe. Acco...
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A new combination method of beam-type finite element multiwavelet-based algorithm and leastsquaresupportvectorregression (LSSVR) algorithm is proposed for detecting the location and size of a crack in a pipe. According to operators of engineering problems, Rayleigh-Euler and Rayleigh-Timoshenko beam-type multiwavelets are constructed using the stable completion in the multiresolution finite element space. A rotational spring model is used for cracked pipe modeling and the local flexibility due to the crack is calculated by discrete approximation method. An adaptive subspace iteration algorithm (ASIA) is applied to efficiently approximate the exact solution of pipe model by adding new beam-type multiwavelets in each scale. To avoid the difficulty of constructing well-defined mathematical models, the normalized crack location and depth is detected by using LSSVR algorithm. The numerical and experimental results verify that the presented method can accurately identify the location and depth of crack in a pipe.
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