Improved lead -acid battery modelling for photovoltaic application by recurrent neural networks

SPEEDAM 2008 - International Symposium on Power Electronics, Electrical Drives, Automation and Motion

Volumn , Issue , 2008, Pages 1170-1174

  Tina, G ^a   Capizzi, G ^a  

^a UNIVERSITY OF CATANIA   (Italy)

Author keywords

Battery modelling; Neural network applications; Nonlinear circuits; Recurrent neural networks

Indexed keywords

ARTIFICIAL INTELLIGENCE; DISCHARGE (FLUID MECHANICS); EQUATIONS OF MOTION; INDUSTRIAL ENGINEERING; MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS; POWER ELECTRONICS; REINFORCEMENT LEARNING;

BASIC VARIABLES; BATTERY MODELLING; CHARGE-DISCHARGE PROCESSES; COMPUTATIONAL BURDEN; DISCHARGE PROCESSES; DYNAMIC BEHAVIORS; DYNAMIC SYSTEMS; ELECTRICAL DRIVES; EXTERNAL INPUT; FEED-FORWARD NETWORKS; INPUT-OUTPUT; INTERNATIONAL SYMPOSIUM; LEAD ACID; LEAD-ACID BATTERIES; MULTILAYER RECURRENT NEURAL NETWORKS; NEURAL NETWORK APPLICATIONS; NON-LINEAR; NON-LINEAR BEHAVIOUR; NONLINEAR CIRCUITS; PHOTOVOLTAIC APPLICATIONS;

RECURRENT NEURAL NETWORKS;

EID: 51849096897     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SPEEDHAM.2008.4581302     Document Type: Conference Paper

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