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卢文联

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简要介绍：

复旦大学数学科学学院数学系教授，在神经网络动力学行为分析以及机器学习和统计等方面均有出色的工作基础。特别地，建立神经高斯随机场的时空模型，并利用数据同化拟合计算神经元网络模型与实验观测数据；国际上最早研究具有不连续激发函数递归神经网络的行为的团队之一,完善了利用不连续激发函数神经网络进行优化计算的理论基础，设计了一类具有不连续右端的神经网络系统，能更精确的求解非光滑代价函数的组合优化问题。获选IEEE Senior Member；2013年入选教育部新世纪人才；获评全国百篇优秀博士学位论文。

代表成果：
1. Centralized and decentralized global outer-synchronization of asymmetric recurrent time-varying neural network by data-sampling.Lu W, Zheng R, Chen T. Neural Networks (2016).
2. A statistical approach for detecting common features.Gan X, Xu B ,Ji X,Lu W, Waxman D, Feng J.Journal of Neuroscience Methods (2015).
3. Achieving cluster consensus in continuous-time networks of multi-agents with inter-cluster non-identical inputs.Han Y,Lu W,Chen T.IEEE Transactions on Automatic Control (2015).
4. Consensus analysis of networks with time-varying topology and event-triggered diffusions.Han Y,Lu W, Chen T.Neural Networks (2015).
5. Consensus in continuous-time multiagent systems under discontinuous nonlinear protocols.Liu B,Lu W, Chen T.IEEE Transactions on Neural Networks and Learning Systems (2015).
6. Pinning networks of coupled dynamical systems with Markovian switching couplings and event-triggered diffusions.Lu W, Han Y, Chen T.Journal of the Franklin Institute (2015)

卢文联

简要介绍：复旦大学数学科学学院数学系教授，在神经网络动力学行为分析以及机器学习和统计等方面均有出色的工作基础。特别地，建立神经高斯随机场的时空模型，并利用数据同化拟合计算神经元网络模型与实验观测数据；国际上最早研究具有不连续激发函数递归神经网络的行为的团队之一,完善了利用不连续激发函数神经网络进行优化计算的理论基础，设计了一类具有不连续右端的神经网络系统，能更精确的求解非光滑代价函数的组合优化问题...

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详细介绍

简要介绍：

代表成果：
1. Centralized and decentralized global outer-synchronization of asymmetric recurrent time-varying neural network by data-sampling.Lu W, Zheng R, Chen T. Neural Networks (2016).
2. A statistical approach for detecting common features.Gan X, Xu B ,Ji X,Lu W, Waxman D, Feng J.Journal of Neuroscience Methods (2015).
3. Achieving cluster consensus in continuous-time networks of multi-agents with inter-cluster non-identical inputs.Han Y,Lu W,Chen T.IEEE Transactions on Automatic Control (2015).
4. Consensus analysis of networks with time-varying topology and event-triggered diffusions.Han Y,Lu W, Chen T.Neural Networks (2015).
5. Consensus in continuous-time multiagent systems under discontinuous nonlinear protocols.Liu B,Lu W, Chen T.IEEE Transactions on Neural Networks and Learning Systems (2015).
6. Pinning networks of coupled dynamical systems with Markovian switching couplings and event-triggered diffusions.Lu W, Han Y, Chen T.Journal of the Franklin Institute (2015)