Traffic time series prediction based on CS and SVR

Wu, Qiong; Zhao, Xiangmo

doi:10.1109/ccdc.2018.8407716

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The bleaching normalization method has been used in this research. In this method, if Xminand Xmax are the minimum and maximum data recorded by the sensor, respectively [75]. In this case,in the range of y min and y max, which is the minimum and maximum desired value in normalization.…”

Section: Improving the Quality Of Time Series Datamentioning

confidence: 99%

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

2024

MJET

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Intelligent traffic management systems, urban planning, and the reduction of traffic congestion all depend on traffic flow prediction. In this research, a method based on machine learning based on neural network combination and feature selection based on genetic algorithm is presented for predicting short-time traffic flow. The genetic algorithm-based approach seeks to find a model's optimal parameters globally. Inner-city traffic constantly changes and can be unpredictable. This is because traffic patterns repeat over time (have periodic characteristics) but also swing wildly from moment to moment (high fluctuations). As a result, it's very hard to guess what traffic will be like in the future. Thin operators have been used duo to it good performance for short-time traffic prediction in neural networks system. In Isfahan gathered traffic data to see how well a new model, called LSTM, predicts traffic flow. We compared LSTM's performance against other established methods like wavelet neural networks (WNN) and multilayer perceptron (MLP). the proposed neural network prediction model and genetic algorithm results have %97 accuracy, %97 correlation coefficient, 14.67 less average absolute error, higher signal-to-noise ratio, 0.97 entropy value, and 3.95 standard deviation. Compared to other methods, it has shown its superiority, such as ordinary neural networks. This model excels at finding the best solution quickly and accurately, even with noisy or complex data.

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Section: Improving the Quality Of Time Series Datamentioning

confidence: 99%

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

2024

MJET

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“…Since Beni and Wang first proposed the concept of swarm intelligence in 1989, swarm intelligence algorithms have been continuously developed and used by many researchers to optimize and find the optimal critical parameters in machine learning models such as SVM, GRNN, and BPNN [36][37][38]. Among intelligent algorithms, the cuckoo search (CS) algorithm has attracted the attention of researchers because of its powerful optimization ability and few adjustment parameters [38]. In particular, the application of CS to SVR in many fields has achieved good effects.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR

Gao

et al. 2022

Sensors

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During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of human movement while being noninvasive. Therefore, in this paper, based on MMG, a combined scheme of gray relational analysis (GRA) and support vector regression optimized by an improved cuckoo search algorithm (ICS-SVR) is proposed to estimate the knee joint extension force. Firstly, the features reflecting muscle activity comprehensively, such as time-domain features, frequency-domain features, time–frequency-domain features, and nonlinear dynamics features, were extracted from MMG signals, and the relational degree was calculated using the GRA method to obtain the correlation features with high relatedness to the knee joint extension force sequence. Then, a combination of correlated features with high relational degree was input into the designed ICS-SVR model for muscle force estimation. The experimental results show that the evaluation indices of the knee joint extension force estimation obtained by the combined scheme of GRA and ICS-SVR were superior to other regression models and could estimate the muscle force with higher estimation accuracy. It is further demonstrated that the proposed scheme can meet the need of muscle force estimation required for rehabilitation devices, powered prostheses, etc.

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An Improved Traffic Flow Prediction Algorithm Based on TLBO and BP

Zhao

2019

2019 Chinese Control and Decision Conference (CCDC)

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Traffic time series prediction based on CS and SVR

Cited by 6 publications

References 12 publications

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR

An Improved Traffic Flow Prediction Algorithm Based on TLBO and BP

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