Abstract
It is still very difficult to exploit possibilistic concepts to identify the strict parameters of vehicular ad-hoc networks (VANET) while minimizing the dispersion of its constraints. In this paper, as a first contribution, an empirical study is presented within the framework of the development of a predictive model based on a linear regression model to interpret the phenomenon of VANET network congestion in a precise way by minimizing the dispersion of predicted outputs. The objective of the model developed is twofold: (1) to model the interactions between the traffic variables involved in the model and to exploit possibilistic concepts to identify the constraints of the VANET network based on a linear optimization under constraints, (2) to anticipate with precision the probability of occurrence of an unforeseen incident on VANET traffic. In addition, the traffic data involved in our model are observed vehicle speed, predicted travel time, observed travel time, and delay time. In addition, the accuracy of the prediction is proven by checking the relevance of the model according to the goodness of fit and the statistical significance of each explained variable. Our second contribution focuses on the design of a new technique for collecting, aggregating, and predicting real-time traffic flow. The main objective is to optimize the prediction error rate under strict conditions whose traffic parameters have unstable values. For this, we propose a traffic flow prediction approach based on fuzzy logic and regression analysis. The approach incorporates approved traffic parameters with the appearance of fuzzy least squares. To increase the prediction accuracy of the traffic flow and then arrive at a quadratic resolution under constraints, we integrate the Fuzzy Support Vector Regression for VANET networks (FSVRNET) model and Unified Particle Swarm Optimization (UPSO) algorithm in our approach. The chosen approach aims to model the interactions between traffic data observed from multiple data sources (e.g., connected loop detectors) to adjust the stability of traffic parameters in the prediction process. Our experimental study shows a strong correlation between the predicted data and the actual state of the VANET traffic flow. In addition, the prediction error of regression analysis is significantly reduced. Prediction performance using UPSO-FSVRNET is better with the proposed test set, as assumed by most identification methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Sellami, L., Alaya, B.: SAMNET: Self-adaptative multi-kernel clustering algorithm for urban VANETs. Vehicular Communications, Vol. 29 (2021)
Abbasi, F., Zarei, M., Rahmani, A.: FWDP: A Fuzzy logic-based vehicle Weighting model for Data Prioritization in vehicular ad hoc networks. Vehicular Communications (2021)
Juan Carlos, F.G., Yurilev, Y.C., Heriberto, R.F.: Distance measures for Interval Type-2 fuzzy numbers. Discret. Appl. Math. 197, 93–102 (2015)
Kandali, K., Bennis, L., Bennis, H.: A new hybrid routing protocol using a modified K-means clustering algorithm and continuous hopfield network for VANET. IEEE Access. 9, 47169–47183 (2021)
Tanaka, H., Lee, H.: Interval regression analysis by quadratic programming approach. IEEE Trans. Fuzzy Syst. 6, 473–481 (1998)
Ontiveros, E., Melin, P., Castillo, O.: Designing hybrid classifiers based on general type-2 fuzzy logic and support vector machines. Soft. Comput. 24(23), 18009–18019 (2020)
Zhang, C., Chen, K., Zeng, X., Xue, X.: misbehavior detection based on support vector machine and Dempster-Shafer theory of evidence in VANETs. IEEE Access 6, 59860–59870 (2018)
Tsai, H.C.: Unified particle swarm delivers high efficiency to particle swarm optimization. Appl. Soft Comput. 55, 371–383 (2017)
Cervantes, J., Lamont, F.G., Rodríguez, L., Lopez-Chau, A.: A comprehensive survey on support vector machine classification: applications, challenges, and trends. Neurocomputing 408, 189–215 (September 2020)
Habshah, M., Jama, M.: A modified Robust support vector regression approach for data containing high leverage points and outliers in the Y-direction. Math. Stat. 8(5), 493–505 (2020)
Tavara, S.: Parallel computing of support vector machines: a survey. ACM Comput. Surv. 51(6), 1231–12338 (2019)
Ahmad, M.S., Adnan, S.M., Zaidi, S., Bhargava, P.: A novel support vector regression (SVR) model for the prediction of splice strength of the unconfined beam specimens. Constr. Build. Mater. 248, 118475 (2020)
Sanchez-Fernandez, M.P., de Prado-Cumplido, M., Arenas-Garcia, J., Perez-Cruz, F.: SVM multiregression for non-linear channel estimation in multiple-input multiple-output systems. IEEE Trans. Signal Process. 58(8), 2298–2307 (2004)
Xue, Z., Zhang, R., Qin, C., Zeng, X.: An adaptive twin support vector regression machine based on rough and fuzzy set theories. Neural Comput. Appl. 32(9), 4709–4732 (2020)
Khan, A.A., Abolhasan, M., Ni, W., Lipman, J., Jamalipour, A.: A hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for resource optimization in 5G VANETs. IEEE Trans. Veh. Technol. 68(7), 6964–6974 (July 2019)
Xiang, X., Wang, X., Zhou, Z.: Self-adaptive on-demand geographic routing for mobile ad hoc networks. IEEE Trans. Mob. Comput. 11(9), 1572–1586 (2012). https://doi.org/10.1109/TMC.2011.177
Lai, W.K., Lin, M., Yang, Y.: A machine learning system for routing decision-making in urban vehicular ad hoc networks. Int. J. Distrib. Sensor Netw. 11(3), 374391 (2015)
Agrawal, S., Raw, R.S., Tyagi, N., Misra, A.K.: Fuzzy logic based greedy routing (flgr) in multi-hop vehicular Adhoc networks. Indian J. Sci. Technol. (2015). https://doi.org/10.17485/ijst/2015/v8i1/70085
Olivas, F., Amaya, I., Ortiz-Bayliss, J.C., Conant-Pablos, S.E., Terashima-Marín, H.: Enhancing hyperheuristics for the knapsack problem through fuzzy logic. Comput. Intell. Neurosci. 2021, 8834324 (2021)
Tal, I., Muntean, G.: Towards reasoning vehicles. ACM Comput. Surv. 50(6), 1–37 (2018)
Cheng, N., Lu, N., Zhang, N., Shen, X.S., Mark, J.W.: Vehicular WiFi offloading: challenges and solutions. Vehic. Commun. 1(1), 13–21 (2014)
Zhou, Y., Li, H., Shi, C., Lu, N., Cheng, N.: A fuzzy-rule based data delivery scheme in VANETs with intelligent speed prediction and relay selection. Wirel. Commun. Mob. Comput. 2018, 1–15 (2018)
Soleymani, S.A., Abdullah, A.H., Anisi, M.H., et al.: BRAIN-F: beacon rate adaption based on fuzzy logic in vehicular ad hoc network. Int. J. Fuzzy Syst. 19, 301–315 (2017)
Zhang, H., Bochem, A., Sun, X., Hogrefe, D.: A security aware fuzzy enhanced reliable ant colony optimization routing in vehicular ad hoc networks. In: Proc. IEEE Intell. Vehicles Symp. (IV), Jun. 2018, pp. 1071–1078 (2018)
Shams, E.A., Rizaner, A., Ulusoy, A.H.: Trust aware support vector machine intrusion detection and prevention system in vehicular ad hoc networks. Comput. Secur. 78, 245–254 (2018)
Ghebleh, R., Ghaffari, A.: A multi-criteria method for resource discovery in distributed systems using deductive fuzzy system. Int. J. Fuzzy Syst. 19, 1829–1839 (2017)
Shengdong, D., Li, T., Gong, X., Horng, S.-J.: A hybrid method for traffic flow forecasting using multimodal deep learning. Int. J. Comput. Intell. Syst. 13, 85–97 (2020)
Alaya, B., Sellami, L.: Clustering method and symmetric/asymmetric cryptography scheme adapted to securing urban VANET networks. J. Inf. Secur. Appl. 58(2), 102779 (2021)
Ranjan, N., Bhandari, S., Zhao, H.P., Kim, H., Khan, P.: City-wide traffic congestion prediction based on CNN, LSTM and transpose CNN. IEEE Access 8, 81606–81620 (2020)
Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 100(1), 9–34 (1999)
Wang, G., Xu, Y., Qin, S.: Basic fuzzy event space and probability distribution of probability fuzzy space. Mathematics. 7(6), 1–15 (2019)
Hao, P.Y., Chiang, J.H.: Fuzzy regressions analysis by support vector learning approach. IEEE Trans. Fuzzy Syst. 16(2), 428–441 (2008)
Škrabánek, P., Marek, J.: Models used in fuzzy linear regression. In: 17th Conference on Applied Mathematics APLIMAT, pp. 954–964 (2018)
Sulistyo, S., Alam, S.: SINR and throughput improvement for VANET using fuzzy power control. Int. J. Commun. Syst. 31(6), e3579 (2018)
Kashani, M., Ghanbari, M., Rahmani, A.: Improving performance of opportunistic routing protocol using fuzzy logic for vehicular ad-hoc networks in highways. J. Artif. Intell. Data Min. 8(10), 1–14 (2020)
Buckley, J., Feuring, T.: Linear and non-linear fuzzy regression: evolutionary algorithm solutions. Fuzzy Sets Syst. 112, 381–394 (2000)
Hao, P., Chiang, J.: Fuzzy regression analysis by support vector learning approach. IEEE Trans. Fuzzy Syst. 16(2), 428–441 (2008)
Wang, H., Wang, J.: An effective image representation method using kernel classification. In: IEEE 26th International Conference on Tools with Artificial Intelligence pp. 853–858 (2014)
Tanaka, H., Lee, H.: Fuzzy linear regression combining central tendency and possibilistic. properties. IEEE. Fuzzy Sets Syst. 24, 063–068 (1997)
Hong, D.H., Hwang, C.: Ridge regression procedures for fuzzy models using triangular fuzzy numbers. Internat. J. Unc. Fuzz. Knowl. Based Syst. 12, 145–159 (2004)
Shi, Y., Zhang, L., Cao, Z., Tanveer, M., Lin, C.-T.: Distributed semi-supervised fuzzy regression with interpolation consistency regularization. IEEE Trans. Fuzzy Syst. (2021). https://doi.org/10.1109/TFUZZ.2021.3104339
Olivas, F., Valdez, F., Castillo, O., Melin, P.: Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic. Soft. Comput. 20(3), 1057–1070 (2016)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. 6th Symp. Micro Mach. Hum. Sci., IEEE Service Center, pp. 39–43 (1995)
Muniyandi, R.C., Hasan, M.K., Hammoodi, M.R., Maroosi, A.: An improved harmony search algorithm for proactive routing protocol in VANET. J. Adv. Transp. 2021, 6641857 (2021)
Yelure, B., Sonavane, S.: Particle swarm optimization based routing method for vehicular ad-hoc network. In: 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 1573–1578
Clerc, M., Kennedy, J.: The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)
Konstantinos, E.P., Michael, N.V.: Particle Swarm Optimization and Intelligence: Advances and Application. Published in the United States of America by Information Science Reference (an imprint of IGI Global), pp. 89–98, (2010).
Wu, Q., Law, R.: Fuzzy support vector regression machine with penalizing Gaussian noises on triangular fuzzy number space. Expert Syst. Appl. 37(12), 7788–7795 (2010)
Peng, D., Xiaoqi, M.A., Zhuanping, W., Yuanfu, M.O., Peng, P.: A prediction method of missing vehicle position information based on least square support vector machine. Sustain. Oper. Comput. 2, 30–35 (2021)
Pahikkala, T., Boberg, J., Salakoski, T.: Fast n-fold cross-validation for regularized least-squares. In: Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006), pp. 83–90, Espoo, Finland (2006)
Darwish, A., Poleshchuk, O., Komarov, E.: A new fuzzy linear regression model for a special case of interval type-2 fuzzy sets. Appl. Math. Inf. Sci. 10, 1209–1214 (2016)
Bisserier, A., Boukezzoula, R., Galichet, S.: Linear fuzzy regression using trapezoidal fuzzy intervals. J. Uncert. Syst. 4(1), 59–72 (2010)
Funding
Funding was provided by Qassim University.
Author information
Authors and Affiliations
Corresponding author
Appendix 1
Appendix 1
j | Type | x 1 | x 2 | x 3 | x 4 | x 5 | y |
|---|---|---|---|---|---|---|---|
1 | Test | 0.36031 | 0.28594 | 0.014864 | 0.8952 | 0.51015 | 1.3915 |
2 | Identification | 0.54851 | 0.39413 | 0.28819 | 0.94239 | 0.71396 | 4.6557 |
3 | Identification | 0.26177 | 0.50301 | 0.81673 | 0.33508 | 0.51521 | 6.2132 |
4 | Outlier | 0.59734 | 0.72198 | 0.98548 | 0.43736 | 0.60587 | 10.5848 |
5 | Test | 0.049278 | 0.30621 | 0.017363 | 0.47116 | 0.9667 | 1.1886 |
6 | Identification | 0.57106 | 0.11216 | 0.81939 | 0.14931 | 0.82212 | 5.658 |
7 | Identification | 0.70086 | 0.44329 | 0.62114 | 0.13586 | 0.31775 | 6.4823 |
8 | Identification | 0.96229 | 0.46676 | 0.56022 | 0.5325 | 0.5877 | 8.2347 |
9 | Identification | 0.75052 | 0.014669 | 0.24403 | 0.72579 | 0.1302 | 1.7014 |
10 | Identification | 0.73999 | 0.66405 | 0.82201 | 0.3987 | 0.25435 | 9.7441 |
11 | Outlier | 0.43187 | 0.72406 | 0.26321 | 0.35842 | 0.80303 | 5.9823 |
12 | Identification | 0.63427 | 0.28163 | 0.75363 | 0.28528 | 0.66785 | 6.4305 |
13 | Identification | 0.80303 | 0.26182 | 0.65964 | 0.86864 | 0.013626 | 6.5469 |
14 | Identification | 0.083881 | 0.70847 | 0.21406 | 0.62641 | 0.56158 | 2.2481 |
15 | Identification | 0.94546 | 0.78386 | 0.60212 | 0.24117 | 0.45456 | 10.919 |
16 | Identification | 0.91594 | 0.98616 | 0.60494 | 0.97808 | 0.90495 | 14.06 |
17 | Identification | 0.60199 | 0.47334 | 0.6595 | 0.6405 | 0.28216 | 7.0714 |
18 | Identification | 0.25356 | 0.90282 | 0.18336 | 0.22985 | 0.065034 | 3.2945 |
19 | Identification | 0.87345 | 0.45106 | 0.63655 | 0.68134 | 0.47659 | 8.2171 |
20 | Identification | 0.5134 | 0.80452 | 0.17031 | 0.66582 | 0.98371 | 6.1764 |
21 | Identification | 0.73265 | 0.82886 | 0.5396 | 0.13472 | 0.92235 | 9.7668 |
22 | Identification | 0.42223 | 0.16627 | 0.62339 | 0.022493 | 0.5612 | 4.162 |
23 | Outlier | 0.96137 | 0.39391 | 0.68589 | 0.2622 | 0.65232 | 9.6279 |
24 | Identification | 0.072059 | 0.52076 | 0.67735 | 0.11652 | 0.77268 | 4.5169 |
25 | Identification | 0.55341 | 0.71812 | 0.87683 | 0.069318 | 0.10618 | 8.4911 |
26 | Test | 0.29198 | 0.56919 | 0.012891 | 0.85293 | 0.0010734 | 1.7484 |
27 | Identification | 0.85796 | 0.46081 | 0.3104 | 0.18033 | 0.54176 | 5.911 |
28 | Identification | 0.33576 | 0.44531 | 0.77908 | 0.032419 | 0.0068578 | 5.4411 |
29 | Identification | 0.6802 | 0.087745 | 0.3073 | 0.73393 | 0.45134 | 2.7881 |
30 | Identification | 0.053444 | 0.44348 | 0.92668 | 0.53652 | 0.19566 | 5.9031 |
31 | Identification | 0.35666 | 0.3663 | 0.67872 | 0.27603 | 0.78714 | 5.6943 |
32 | Test | 0.4983 | 0.30253 | 0.074321 | 0.36846 | 0.61856 | 2.3165 |
33 | Test | 0.43444 | 0.85184 | 0.070669 | 0.012886 | 0.015521 | 4.0561 |
34 | Test | 0.56246 | 0.75948 | 0.01193 | 0.88921 | 0.89085 | 5.1463 |
35 | Identification | 0.61662 | 0.94976 | 0.22715 | 0.86602 | 0.7617 | 7.9658 |
36 | Outlier | 0.11334 | 0.55794 | 0.51625 | 0.25425 | 0.90704 | 4.3186 |
37 | Identification | 0.89825 | 0.014233 | 0.4582 | 0.56948 | 0.75857 | 3.5161 |
38 | Identification | 0.75455 | 0.59618 | 0.7032 | 0.15926 | 0.38073 | 8.3834 |
39 | Identification | 0.79112 | 0.81621 | 0.58248 | 0.59436 | 0.33111 | 10.172 |
40 | Identification | 0.81495 | 0.97709 | 0.50921 | 0.3311 | 0.50408 | 11.1 |
41 | Test | 0.67 | 0.22191 | 0.07429 | 0.65861 | 0.56457 | 2.2748 |
42 | Identification | 0.20088 | 0.70368 | 0.19324 | 0.86363 | 0.7672 | 3.3021 |
43 | Identification | 0.27309 | 0.52206 | 0.3796 | 0.56762 | 0.77987 | 4.3628 |
44 | Identification | 0.62623 | 0.9329 | 0.27643 | 0.98048 | 0.4841 | 8.0007 |
45 | Identification | 0.53685 | 0.71335 | 0.77088 | 0.79183 | 0.80221 | 9.5484 |
46 | Identification | 0.059504 | 0.22804 | 0.31393 | 0.15259 | 0.47101 | 2.023 |
47 | Outlier | 0.088962 | 0.44964 | 0.63819 | 0.83303 | 0.20276 | 4.9731 |
48 | Identification | 0.27131 | 0.1722 | 0.98657 | 0.19186 | 0.57961 | 6.1146 |
49 | Identification | 0.40907 | 0.96882 | 0.50288 | 0.63899 | 0.6665 | 7.5644 |
50 | Identification | 0.47404 | 0.35572 | 0.9477 | 0.669 | 0.67677 | 8.1508 |
51 | Identification | 0.90899 | 0.049047 | 0.82803 | 0.77209 | 0.94251 | 6.7529 |
52 | Identification | 0.59625 | 0.75534 | 0.91756 | 0.37982 | 0.77015 | 10.382 |
53 | Identification | 0.32896 | 0.89481 | 0.11308 | 0.44159 | 0.7374 | 4.1526 |
54 | Identification | 0.47819 | 0.28615 | 0.81213 | 0.48306 | 0.86626 | 6.964 |
55 | Identification | 0.59717 | 0.2512 | 0.90826 | 0.60811 | 0.99095 | 8.128 |
56 | Outlier | 0.16145 | 0.93274 | 0.15638 | 0.176 | 0.50393 | 3.0023 |
57 | Identification | 0.82947 | 0.13098 | 0.12212 | 0.002026 | 0.62909 | 2.0933 |
58 | Outlier | 0.95612 | 0.94082 | 0.76267 | 0.79022 | 0.79261 | 15.0394 |
59 | Identification | 0.59555 | 0.70185 | 0.7218 | 0.51361 | 0.44865 | 8.7316 |
60 | Identification | 0.028748 | 0.84768 | 0.65164 | 0.21323 | 0.52436 | 4.0547 |
61 | Identification | 0.81212 | 0.20927 | 0.75402 | 0.10345 | 0.17147 | 5.655 |
62 | Identification | 0.61011 | 0.45509 | 0.66316 | 0.15734 | 0.13067 | 6.3181 |
63 | Identification | 0.70149 | 0.081074 | 0.88349 | 0.40751 | 0.21878 | 5.7541 |
64 | Outlier | 0.092196 | 0.85112 | 0.27216 | 0.40776 | 0.10548 | 3.5862 |
65 | Outlier | 0.42489 | 0.56205 | 0.41943 | 0.052693 | 0.14143 | 5.0938 |
66 | Identification | 0.37558 | 0.3193 | 0.21299 | 0.94182 | 0.45697 | 2.8742 |
67 | Test | 0.16615 | 0.3749 | 0.0356 | 0.14997 | 0.78813 | 1.4327 |
68 | Identification | 0.83315 | 0.8678 | 0.081164 | 0.38437 | 0.28106 | 7.7773 |
69 | Identification | 0.83864 | 0.37218 | 0.85057 | 0.31106 | 0.22479 | 7.9538 |
70 | Identification | 0.45161 | 0.07369 | 0.3402 | 0.16853 | 0.90887 | 2.9745 |
71 | Identification | 0.9566 | 0.19984 | 0.46615 | 0.89665 | 0.007329 | 5.0784 |
72 | Identification | 0.14715 | 0.049493 | 0.91376 | 0.32272 | 0.58874 | 5.578 |
73 | Identification | 0.86993 | 0.56671 | 0.22858 | 0.734 | 0.54212 | 6.7023 |
74 | Identification | 0.76944 | 0.12192 | 0.86204 | 0.4109 | 0.65352 | 6.3838 |
75 | Identification | 0.44416 | 0.52211 | 0.65662 | 0.39979 | 0.31343 | 6.2254 |
76 | Identification | 0.62062 | 0.11706 | 0.89118 | 0.50552 | 0.23116 | 6.1369 |
77 | Identification | 0.95169 | 0.76992 | 0.48814 | 0.16931 | 0.41606 | 10.106 |
78 | Identification | 0.64001 | 0.37506 | 0.99265 | 0.52475 | 0.2988 | 8.4947 |
79 | Identification | 0.24733 | 0.82339 | 0.37333 | 0.6412 | 0.67244 | 4.8341 |
80 | Identification | 0.3527 | 0.046636 | 0.53138 | 0.016197 | 0.93826 | 3.7189 |
81 | Identification | 0.18786 | 0.59791 | 0.18132 | 0.83685 | 0.34315 | 2.4511 |
82 | Identification | 0.49064 | 0.94915 | 0.50194 | 0.80346 | 0.56296 | 8.2901 |
83 | Outlier | 0.40927 | 0.2888 | 0.42219 | 0.69778 | 0.11889 | 4.2938 |
84 | Identification | 0.46353 | 0.88883 | 0.66043 | 0.46189 | 0.16902 | 8.0608 |
85 | Identification | 0.61094 | 0.10159 | 0.67365 | 0.082613 | 0.2789 | 4.178 |
86 | Identification | 0.071168 | 0.065315 | 0.95733 | 0.82072 | 0.55681 | 6.7146 |
87 | Identification | 0.31428 | 0.2343 | 0.19187 | 0.19302 | 0.48559 | 2.0056 |
88 | Identification | 0.60838 | 0.9331 | 0.11122 | 0.44535 | 0.95222 | 7.2387 |
89 | Identification | 0.17502 | 0.063128 | 0.56505 | 0.012958 | 0.23192 | 3.0042 |
90 | Identification | 0.62103 | 0.26422 | 0.96917 | 0.30874 | 0.47866 | 7.3143 |
91 | Test | 0.24596 | 0.99953 | 0.023744 | 0.87535 | 0.52652 | 2.896 |
92 | Identification | 0.58736 | 0.21199 | 0.87022 | 0.83526 | 0.79272 | 7.6784 |
93 | Test | 0.50605 | 0.49841 | 0.026877 | 0.3331 | 0.19301 | 2.7117 |
94 | Identification | 0.46478 | 0.29049 | 0.51953 | 0.88071 | 0.9096 | 5.6903 |
95 | Identification | 0.54142 | 0.67275 | 0.19229 | 0.47969 | 0.9222 | 5.6388 |
96 | Identification | 0.94233 | 0.95799 | 0.71569 | 0.56082 | 0.013266 | 13.409 |
97 | Identification | 0.34176 | 0.76655 | 0.25067 | 0.61591 | 0.76755 | 4.771 |
98 | Identification | 0.4018 | 0.66612 | 0.93386 | 0.6619 | 0.94734 | 9.4795 |
99 | Identification | 0.30769 | 0.13094 | 0.13719 | 0.61663 | 0.81331 | 1.9195 |
100 | Identification | 0.41157 | 0.095413 | 0.52162 | 0.68514 | 0.92383 | 4.569 |
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sellami, L., Alaya, B. UPSO-FSVRNET: Fuzzy Identification Approach in a VANET Environment Based on Fuzzy Support Vector Regression and Unified Particle Swarm Optimization. Int. J. Fuzzy Syst. 25, 743–762 (2023). https://doi.org/10.1007/s40815-022-01408-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-022-01408-7