An assessment of qualitative performance of machine learning architectures: Modular feedback networks

IEEE Transactions on Neural Networks

Volumn 19, Issue 1, 2008, Pages 183-189

  Chen, Mo ^a   Gautama, Temujin ^b   Mandic, Danilo P ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b PHILIPS RESEARCH LABORATORIES   (Netherlands)

Author keywords

Delay vector variance; Nonlinearity; Pipelined recurrent neural networks (PRNNs); Qualitative performance; Sensitivity

Indexed keywords

COMPUTER SIMULATION; RECURRENT NEURAL NETWORKS; SENSITIVITY ANALYSIS; VECTORS;

COMPREHENSIVE SIMULATIONS; DELAY VECTOR VARIANCE (DVV); MODULAR FEEDBACK NETWORKS; PIPELINED RECURRENT NEURAL NETWORK;

LEARNING SYSTEMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; FEEDBACK SYSTEM; HUMAN; NONLINEAR SYSTEM; SIGNAL PROCESSING; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FEEDBACK; HUMANS; LINEAR MODELS; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 39549092149     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2007.902728     Document Type: Article

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