Wavelet neural network with improved genetic algorithm for traffic flow time series prediction

Yang, Huazhong; Hu, Xu

doi:10.1016/j.ijleo.2016.06.017

Cited by 97 publications

(42 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The current methods will be used to improve the performance of RBF networks, which include fuzzy algorithms and intelligent optimization algorithms [24][25][26][27]. In fuzzy systems, the design of fuzzy sets, membership functions, and fuzzy rules is based on empirical knowledge, and the algorithm itself does not have the ability to learn autonomously [25].…”

Section: Optimization Of the Rbf Neural Network Based On The Genetic mentioning

confidence: 99%

“…In fuzzy systems, the design of fuzzy sets, membership functions, and fuzzy rules is based on empirical knowledge, and the algorithm itself does not have the ability to learn autonomously [25]. However, the intelligent optimization algorithm represented by the genetic algorithm [26,27] can realize self-learning by using the law of biological evolution, ultimately converging to the most adaptive group to obtain the optimal solution or most satisfactory solution.…”

Section: Optimization Of the Rbf Neural Network Based On The Genetic mentioning

confidence: 99%

“…where C 2 = α 0 ε 2 π 0 2 g + σ 2 W * 2 . The first term in Equation (27) gradually decays to 0 + over time. For this reason, as long as the second term is guaranteed to be a very small amount, it can be guaranteed that when t → ∞ , V → 0 + .…”

Section: Ideal Sliding Mode Control Based On the Rbf Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

Wang

Li²,

Miao

2020

JMSE

View full text Add to dashboard Cite

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts.

show abstract

Section: Optimization Of the Rbf Neural Network Based On The Genetic mentioning

confidence: 99%

Section: Optimization Of the Rbf Neural Network Based On The Genetic mentioning

confidence: 99%

See 1 more Smart Citation

Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

Wang

Li²,

Miao

2020

JMSE

View full text Add to dashboard Cite

show abstract

“…It is exactly the problem that wavelet neural network can solve. Wavelet neural network has the good capability of localization and nonlinear mapping [16]. Moreover, particle swarm optimization can further improve the convergence rate and parameter iteration process.…”

Section: Study Modelmentioning

confidence: 99%

Two-Phase Model of Multistep Forecasting of Traffic State Reliability

Yang

Liu

Jiang

et al. 2018

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Multistep prediction of traffic state is a key technology for advanced transportation information system. The research results based on the principle of multistep prediction can provide more information about the traffic operating quality in advance. Considering that prediction error increases with the increasing numbers of multistep predictions, this research proposes the concept of dynamic predictability that is related to the characteristic of historical traffic flow data used. The traffic flow is characterized by randomness, regularity, and volatility according to the traffic flow theory. Therefore, three key indexes are firstly calculated to measure the characteristics of reliability series. Then a two-phase model is established based on wavelet neural network optimized by particle swarm optimization. The upper phase is a model to estimate the number of predictable steps, and the lower phase is the multistep prediction model of reliability. Compared with that of backpropagation neural network and support vector machine, results show that the convergence time of the wavelet neural network optimized by particle swarm optimization is the lowest, which only costs 256 and 291 seconds in both two-phase models under the same conditions. The average relative error of multistep prediction reached the lowest value, 8.91% and 12.01%, respectively, for weekday and weekend data used. Moreover, the prediction performance based on weekday is better than that of weekend. The research results lay a decision-making basis for managers in determining the key parts of road network to develop future improvement measures.

show abstract

“…GA can solve large optimization problems with large search spaces and it has been used e.g. to solve routing problems [9,10] and for network traffic prediction [11,12,13]. An important feature of GA is that it provides a near-optimal solution in quick time.…”

Section: Introductionmentioning

confidence: 99%

Predicting expected TCP throughput using genetic algorithm

2016

View full text Add to dashboard Cite

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.

show abstract

Wavelet neural network with improved genetic algorithm for traffic flow time series prediction

Cited by 97 publications

References 22 publications

Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

Two-Phase Model of Multistep Forecasting of Traffic State Reliability

Predicting expected TCP throughput using genetic algorithm

Contact Info

Product

Resources

About