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Shi T, Chen S. Robust Twin Support Vector Regression with Smooth Truncated Hε Loss Function. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11198-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Wang L, Ma Y, Chang X, Gao C, Qu Q, Chen X. Projection wavelet weighted twin support vector regression for OFDM system channel estimation. Artif Intell Rev 2020. [DOI: 10.1007/s10462-020-09853-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
AbstractIn this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading in both time and frequency domains. The PWWTSVR utilizes pilot signals to estimate response of nonlinear wireless channel, which is the main work area of SVR. Projection axis in optimal objective function of PWWRSVR is sought to minimize the variance of the projected points due to the utilization of a priori information of training data. Different from traditional support vector regression algorithm, training samples in different positions in the proposed PWWTSVR model are given different penalty weights determined by the wavelet transform. The weights are applied to both the quadratic empirical risk term and the first-degree empirical risk term to reduce the influence of outliers. The final regressor can avoid the overfitting problem to a certain extent and yield great generalization ability for channel estimation. The results of numerical experiments show that the propose algorithm has better performance compared to the conventional pilot-aided channel estimation methods.
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Zerhari B, Lahcen AA, Mouline S. MIPCNF: Multi-iterative partitioning class noise filter. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-190261] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Btissam Zerhari
- LRIT, Associated Unit to CNRST-URAC no. 29, Faculty of Science, Rabat IT Center, Mohammed V University in Rabat, Morocco
| | - Ayoub Ait Lahcen
- LRIT, Associated Unit to CNRST-URAC no. 29, Faculty of Science, Rabat IT Center, Mohammed V University in Rabat, Morocco
- LGS, National School of Applied Sciences (ENSA), Ibn Tofail University, Kenitra, Morocco
| | - Salma Mouline
- LRIT, Associated Unit to CNRST-URAC no. 29, Faculty of Science, Rabat IT Center, Mohammed V University in Rabat, Morocco
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Srivastava S, Malik H, Sharma R. Special issue on intelligent tools and techniques for signals, machines and automation. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-169773] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Smriti Srivastava
- Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT) Delhi, New Delhi, India
| | - Hasmat Malik
- Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT) Delhi, New Delhi, India
| | - Rajneesh Sharma
- Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT) Delhi, New Delhi, India
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