A New Optimization Neural Network for High Resolution Time-Frequency Analysis

作     者：Z.S. Wang,Y.S.Xia,J.D.Z. Chen,W.H.Li,Z.Y.He,(Dept. of Radio Engineering, Southeast University, Nanjing, 210096)(Dept. of Mathematics,Nanjing University of Posts & Telecommunications)(Institute for Healthcare Researc, Baptist Medical Center, Oklahoma City, 

出 版 物：《电路与系统学报》 (Journal of Circuits and Systems)

年 卷 期：1997年第1期

页      面：1-7页

核心收录：

学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：Neural network Optimization Overcomplete signal representation Basis pursuit Wavelet Time-frequency analysis 

摘      要：The authors present a new optimization neural network, which can be called as constrained smallest l1-norm neural network (CSl1NN), to implement the Basis Pursuit(BP)[1][2][3]for high resolution time-frequency analysis. As the new and generalized one of the communities of overcomplete signal representations, the BP is considered as a large-scale linear programming problem. In contrast with the Simplex-BP or Interior-BP in [2],the proposed CSl1NN-BP dose not double the optimizing scale and can be implemented in real time through hardware. Taking non-stationary artificial signals and Electrogastrograms (EGGs) to test, our simulations show that the CSl1 NN presents an excellent convergence performance for a wide range of time-frequency(TF)dictionaries and has a higher joint TF resolution not only than the traditional Wigner Distribution (WD), but also than recently rising other overcomplete representation methods, such as Method of Frames (MOF)[9], Best Orthogonal Basis (BOB)[10] and Matching Pursuit (MP)[4]. Combining the high resolution with the fast implementation,the CSl1NN-BP will be very promising for on-line time frequency analysis of various kinds of non-stationary signals,such as linear chirp,qUadric chirp,bumps etc., and medical signals,such as ECG,EEGand EGG,etc., with high quality.