Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks

IEEE Transactions on Neural Networks

Volumn 22, Issue 5, 2011, Pages 714-726

  Cheng, Long ^a   Hou, Zeng Guang ^a   Lin, Yingzi ^b   Tan, Min ^a   Zhang, Wenjun Chris ^c   Wu, Fang Xiang ^c  

^a INSTITUTE OF AUTOMATION   (China)

^b Northeastern University   (United States)

^c UNIVERSITY OF SASKATCHEWAN   (Canada)

Author keywords

Convex; genetic regulatory network; identification; non smooth optimization problem; recurrent neural network

Indexed keywords

CONVEX; CONVEX OPTIMIZATION PROBLEMS; EQUILIBRIUM POINT; EQUILIBRIUM POINT SET; GENERALIZED GRADIENTS; GENETIC REGULATORY NETWORKS; IDENTIFICATION; IDENTIFICATION ACCURACY; IDENTIFICATION PROBLEM; INEQUALITY CONSTRAINT; LAGRANGIAN; LINEAR EQUALITY CONSTRAINTS; NEURAL NETWORK MODEL; NON-SMOOTH; NONSMOOTH OPTIMIZATION; OBJECTIVE FUNCTIONS; OPTIMAL SOLUTIONS; OPTIMIZATION PROBLEMS; PENALTY METHODS; SADDLE POINT; SIMULATION RESULT;

CONSTRAINT THEORY; CONVEX OPTIMIZATION; OPTIMIZATION; QUADRATIC PROGRAMMING; THEOREM PROVING;

RECURRENT NEURAL NETWORKS;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGY; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; HUMAN; METHODOLOGY; PROBLEM SOLVING; STANDARD;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; HUMANS; NEURAL NETWORKS (COMPUTER); PROBLEM SOLVING;

EID: 79955864876     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2109735     Document Type: Article

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