{"title": "ZNN", "fields": ["discretization", "convolutional neural network", "speedup", "generalization", "computation", "drazin inverse", "task parallelism", "noise measurement", "manipulator", "matrix", "numerical differentiation", "cache", "error function", "recurrent neural network", "kinematics", "computer science", "noise reduction", "activation function", "artificial neural network", "jacobian matrix and determinant", "lyapunov function", "numerical analysis", "zhang", "residual", "inverse", "mathematical optimization", "parallel algorithm", "generalized inverse", "quadratic programming", "redundancy", "machine learning", "exponential growth", "moore penrose pseudoinverse", "artificial intelligence", "convolutional code"], "abstract": "As a special class of recurrent neural network, Zhang neural network (ZNN) has been recently proposed since 2001 for solving various time-varying problems, and has shown high efficiency and excellent performance for solving the problems in the real domain. In this paper, to solve online the time-varying complex generalized inverse (in most cases, the pseudoinverse) problem in the complex domain, a new type of complex-valued ZNN is further proposed and investigated. The design of such a complex ZNN is based on a complex Zhang function (ZF) which is indefinite and quite different from the usual error function (specially, the scalar-valued energy function) in the studies of conventional algorithms. By introducing five different complex ZFs, five different complex ZNN models (termed complex ZNN-I, ZNN-II, ZNN-III, ZNN-IV, and ZNN-V models) are proposed, developed, and investigated for the online solution of the time-varying complex generalized inverse matrices. Theoretical results of convergence analysis are presented to show the desirable properties of complex ZNN models. In addition, we discover the link between the proposed complex ZNN models and the Getz-Marsden dynamic system in the complex domain. Computer-simulation results further demonstrate the effectiveness of complex ZNN models based on different complex ZFs for the time-varying complex generalized inverse matrices.", "citation": "Citations (68)", "departments": ["Sun Yat-sen University", "Sun Yat-sen University", "Sun Yat-sen University", "Sun Yat-sen University", "Sun Yat-sen University", "Sun Yat-sen University", "Sun Yat-sen University", "Massachusetts Institute of Technology", "Massachusetts Institute of Technology", "Princeton University", "Carnegie Mellon University", "Carnegie Mellon University", "Carnegie Mellon University", "Sun Yat-sen University", "Sun Yat-sen University", "Hong Kong Polytechnic University", "Sun Yat-sen University", "Sun Yat-sen University", "Sun Yat-sen University", "Sun Yat-sen University", "Key Laboratory  ...  510640, China", "Key Laboratory  ...  510640, China", "Key Laboratory  ...  510640, China", "Key Laboratory  ...  510640, China"], "authors": ["<NAME>.....http://dblp.org/pers/hd/z/Zlateski:Aleksandar", "<NAME>.....http://dblp.org/pers/hd/l/Lee:Kisuk", "<NAME>.....http://dblp.org/pers/hd/s/Seung:H=_Sebastian"], "conf": "sc", "year": "2016", "pages": 12}