福州大学数学与计算机科学学院计算机图形学与多媒体/人工智能研究生导师介绍：夏又生

导师信息来源：福州大学数学与计算机科学学院 2019-09-03　相关院校：福州大学

福州大学数学与计算机科学学院计算机图形学与多媒体/人工智能研究生导师夏又生介绍如下：

基本信息

姓名夏又生

职称教授

职务博士生导师

主讲课程数值计算方法，人工神经网络，神经动力学优化算法及应用，神经计算原理及应用

研究方向智能计算与模式识别

办公室数计学院2号楼106

电子邮件 ysxia@fzu.edu.cn

联系电话 0591-22865171

个人简介

研究兴趣:图像配准与融合,盲图像恢复与重建,图像超分辨,随机优化算法

讲授课程

本科生：数值计算方法，人工神经网络等

硕士生：神经动力学优化算法及应用，神经计算原理及应用

主持科研项目

(1) 基于广义约束最小绝对偏差估计的盲图像恢复神经网络算法,国家自然科学基金面上项目，2009.1-2011.12。

(2) 有色噪声下基于噪声约束最小均方估计的语音增强算法, 国家自然科学基金面上项目，2012.1-2015.12。

获奖

基于优化的非线性系统智能建模与控制，2010年高等学校科学研究优秀成果奖自然科学奖二等奖（排名第二）

主要论文

1.系统参数辩识与语音增强研究工作

[1] 夏又生, Z.P. Deng，X. Zheng, Analysis and application of a novel fast algorithm for 2-D ARMA model parameter estimation, Automatica, vol. 49, pp 3056-3064, 2013.

[2] 夏又生, M. Kamel, and H. Leung ,A Fast Algorithm for AR Parameter Estimation Using A Novel Constrained Least Squares method，Neural Networks, vol. 33, pp. 396-405, 2010.

[3] 夏又生 M. Kamel, A generalized least absolute deviation method for parameter estimation of autoregressive signals, IEEE Transactions on Neural Networks,vol.19,107-118, 2008 .

[4] Y.S. Xia, L. Henry, and N. Xie ``A New Regression estimator with neural network realization", IEEE Transactions on Signal Processing, vol. 53, pp.672-685, 2005.

[5] 陈志庆, 夏又生,A Fast Algorithm For Vector ARMA Parameter Estimation, International Conference on Electrical and Engineering and Automatic Control, vol. 3, pp178-181, Zibo, China, 2010.

[6] 夏又生, 俞颖, Speech Enhancement Using Generalized Least Absolute Deviation Estimation，International Conference on Audio, Language and Image Processing，vol. 1, pp. 64-68, Shanghai, China， 2010 .

2. 数据融合研究工作:

[1] 夏又生 and H. Leung，Performance analysis of statistical optimal data fusion algorithms, Information Science, 2014.

[2] 夏又生 and H. Leung，A Fast Learning Algorithm for Blind Data Fusion Using A Novel L2-Norm Estimation，IEEE Sensors Journal, vol.14, pp. 666 – 672, 2014.

[3] 夏又生 M. Kamel, Cooperative learning algorithms for data fusion using novel L1 estimation, IEEE Transactions on Signal Processing, vol. 56, 1083-1095,2008 .

[4] 夏又生 and M. Kamel, A Measurement Fusion Method for Nonlinear System Identification and Its Cooperative Learning Algorithm , Neural Computation, Vol. 19, pp. 1589-1632, 2007.

[5] 夏又生, Leung H, Nonlinear spatial-temporal prediction based on optimal fusion, IEEE TRANSACTIONS ON NEURAL NETWORKS , vol. 17 , 975-988 Published: 2006

3. 图像恢复与重建研究工作

[1] 夏又生，Sun C., and Zheng WX, A Discrete-Time Neural Network for Fast Solving Large Linear L1-norm Estimation Problems and its Application to Image Restoration, IEEE Transactions on Neural Networks and Learning Systems, vol.23 , pp. 812 - 820 , 2012

[2] 石泉斌，夏又生*，Fast multi-channel image reconstruction using a novel two-dimensional algorithm, Multimedia Tools and Applications , vol. 63, Feb, 2013 online Published。

[3] 夏又生, Z.P. Deng，X. Zheng, Analysis and application of a novel fast algorithm for 2-D ARMA model parameter estimation, Automatica, vol. 49, pp 3056-3064, 2013.

[4] 夏又生 and M. Kamel, Novel cooperative neural fusion algorithms for image restoration and image fusion, IEEE Transactions on Image Processing, vol. 16, pp. 367-381, 2007 .

[5] 夏又生, A fast algorithm for constrained GLAD estimation with application to image restoration, Proceeding of World Congress on Intelligent Control
and Automation, July, Jinan, China, pp.729-734,2010.

[6] 庄金莲,夏又生, An Improved Regularization Approach for Blind Restoration of multichannel Imagery，International Congress on Image and Signal Processing，上海，2011年10月。

[7] 文虎儿，夏又生, A SIFT operator -based Image Registration Using Cross-Correlation Coefficient, International Congress on Image and Signal Processing，上海，2011年10月。

4．智能优化算法的研究工作

[1] 夏又生，陈天平，J. Shan，A novel iteration method for computing generalized inverse，Neural Computation, vol.26, pp. 449-465 , 2014

[2] 夏又生, A Compact Cooperative Recurrent Neural Network for Computing General Constrained L-1 Norm Estimators，IEEE Transactions on Signal Processing， vol.57, pp. 3693-3697,2009。

[3] 夏又生, G. Feng, and J. Wang, ``A novel recurrent neural network for solving nonlinear optimization problems with inequality constraints”, IEEE Transactions on Neural Networks, vol.19,1340-1353, 2008

[4] 夏又生and M. Kamel, ``Cooperative Recurrent Neural Networks for solving L1 estimation problems with general linear constraints," Neural Computation, vol. 20, pp.844-872, 2008.

[5] 夏又生, New cooperative recurrent neural networks for solving constrained variational inequality problems， Science in China: Information sciences, vol.52, 1766-177, 2009.

[6] Sun C., 夏又生, An analysis of a neural dynamical approach to solving optimization Problems，IEEE Transactions on Automatic Control,vol. 54, pp. 1972-1977,2009.

[7] 夏又生and G. Feng, ``A New Neural Network for Solving
Nonlinear Projection equations," Neural Networks, vol. 20, pp. 577-589, 2007.

[8] 夏又生, G. Feng, and M. S Kamel, Development and analysis of neural dynamical approaches to solving nonlinear programming problems, IEEE Transactions on Automatic Control, Vol 52, pp. 2154-2159, 2007.

[9] 夏又生, G. Feng, and J. Wang, ``A Primal-Dual Neural Network
for On-Line Resolving Constrained Kinematic Redundancy," IEEE Transactions on Systems, Man and Cybernetics - Part B, vol. 35, pp. 54-64, 2005.

[10] 夏又生, ``An extended projection neural network for constrained optimization", Neural Computation, vol. 16, no. 4, pp. 863-883, 2004.

[11] 夏又生, ``Further results on global convergence and stability of globally projected dynamical systems," Journal of Optimization Theory and Applications, vol. 122, pp.627-149, 2004.

[12] 夏又生 and F. Gang, ``On Convergence Rate of Projection Neural
Networks," IEEE Transactions on Automatic Control, vol.
49, pp. 91-96, 2004.

[13] 夏又生and J. Wang, ``A General Projection neural network for solving monotone variational inequality and related optimization problems", IEEE Transactions Neural Networks}, vol.15, pp. 318-328, 2004.

[14] 夏又生,J. Wang, and L. M. Fork, ``Grasping Force Optimization for Multifingered Robotic Hands Using a Recurrent Neural Network," IEEE Transactions on Robotics and Automation, vol. 20, pp. 549-554, 2004.