FPGA Implementation of Wavelet Neural Network Training with PSO/iPSO

Şahin, Sühap; Çavuşlu, Mehmet Ali

doi:10.1142/s0218126618500986

Cited by 9 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each member of the group represents a feasible solution, and the location of the food is considered to be the global optimal solution. Each member learns from the personal best position (p-best) and the global best position (g-best) in the population, and finally approaches the position of the global optimal solution [24] . The mathematical expression of PSO algorithm is given as following:…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Reliable short-term prediction of available parking space (APS) is the basic theory of parking guidance information system (PGIS). Based on the Intelligent parking system at the Eastern New Town, Yinzhou District, Ningbo, China, this study collected the data of parking availability in the on-street parking areas. The variation characteristics of APS were investigated and analyzed at different spatialtemporal levels. Then the APS prediction models based on Gradient Boosting Decision Tree (GBDT) and Wavelet Neural Network (WNN) were proposed. Furthermore, an improved WNN algorithm with (WA) decomposition and Particle Swarm Optimization (PSO) were presented. The original time series was decomposed and reconstructed by wavelet analysis, and the WNN algorithm found the optimal threshold of initial weight through PSO. The result of GBDT (weekday: MSE=27.37, S MSE =0, TIME=35min, weekend: MSE=9.9, S MSE =0,TIME=35min) and WA-PSO-WNN (weekday: MSE=14.93,S MSE =1.88, TIME=160.32s, weekend: MSE=12.33, S MSE =10.23, TIME=160.95s) approximated the true value. But the prediction time of GBDT was too long to be applicable to the short-term prediction of APS in this paper. Compared with the methods of GBDT, WNN, and PSO-WNN, the WA-PSO-WNN algorithm performs much better. The average differences in MSE between WA-PSO-WNN and GBDT for weekday and weekend data are 45.45% and 58.76%, respectively, indicating that WA-PSO-WNN can increase the prediction accuracy of weekday and weekend data by an average of 45.45% and 58.76% compared with the GBDT model. Finally, the application prospects of short-term APS forecasting were also discussed in reducing cruising parking behavior, reducing illegal parking behavior and adjusting dynamic parking rates to verify the importance of APS short-term forecasting.

show abstract

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In recent years, Zekri et al [17][18][19] studied the combination of wavelet theory and the fuzzy neural network (FNN). In the FWNN, fuzzy rules are corresponding to the sub-WNN, respectively, and the wavelet and fuzzy sets parameters learning can improve the FWNN approximation accuracy [20][21][22][23]. However, the main drawback of the WNN is that due to its feed-forward network structure, its application area is limited to static issues.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Wavelet Neural Network with the Improved Levenberg–Marquardt Algorithm for the AC Servo System

et al. 2021

View full text Add to dashboard Cite

In this study, a fuzzy wavelet neural network with the improved Levenberg–Marquardt algorithm (FWNN-LM) is proposed to conquer nonlinearity and uncertain disturbance problems in the AC servo system. First of all, use the particle swarm optimization algorithm based on Levenberg–Marquardt (LM) to optimize parameters in the FWNN controller. Second, the potentiality of fuzzy rules (PFR) method is developed to optimize the structure of the FWNN by error reduction ratio (ERR). Furthermore, stability of FWNN-LM is proved by the Lyapunov method. Finally, simulation and prototype test results show that this method can improve the accuracy and robustness of the system in presence of load disturbances and parameter perturbations.

show abstract

“…However, like most swarm intelligence algorithms, this algorithm can be easily trapped into local optima in later generations or iterations, and the search process may premature. Many works have been done to improve the standard or traditional PSO algorithm [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems

Wang

Liu

Sun

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A new method using collective responses of starling birds is developed to enhance the global search performance of standard particle swarm optimization (PSO). The method is named chaotic starling particle swarm optimization (CSPSO). In CSPSO, the inertia weight is adjusted using a nonlinear decreasing approach and the acceleration coefficients are adjusted using a chaotic logistic mapping strategy to avoid prematurity of the search process. A dynamic disturbance term (DDT) is used in velocity updating to enhance convergence of the algorithm. A local search method inspired by the behavior of starling birds utilizing the information of the nearest neighbors is used to determine a new collective position and a new collective velocity for selected particles. Two particle selection methods, Euclidean distance and fitness function, are adopted to ensure the overall convergence of the search process. Experimental results on benchmark function optimization and classic clustering problems verified the effectiveness of this proposed CSPSO algorithm.

show abstract

FPGA Implementation of Wavelet Neural Network Training with PSO/iPSO

Cited by 9 publications

References 22 publications

Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches

Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches

Fuzzy Wavelet Neural Network with the Improved Levenberg–Marquardt Algorithm for the AC Servo System

A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems

Contact Info

Product

Resources

About