. "2009-04-20+02:00"^^ . "978-80-7204-629-4" . "Vyu\u017Eit\u00ED neuronov\u00FDch s\u00EDt\u00EDch v aplikac\u00EDch technick\u00FDch za\u0159\u00EDzen\u00ED budov"@cs . "XII Mezin\u00E1rodn\u00ED v\u011Bdeck\u00E1 konference Technick\u00E1 za\u0159\u00EDzen\u00ED staveb a energie budov" . . "Z(MSM6840770003)" . "ARTIFICIAL NEURAL NETWORKS UTILIZATION WITHIN BUILDING ENERGY AND ENVIRONMENTAL SYSTEMS" . . . . . . "ARTIFICIAL NEURAL NETWORKS UTILIZATION WITHIN BUILDING ENERGY AND ENVIRONMENTAL SYSTEMS"@en . "[519D20129BB9]" . "304101" . "Information technology progress influences all of our life. Modern buildings are significant example of formation of indoor climate conditions including all HVAC systems within a building. As our qualitative requirements increase, grows also importance of building energy performance as well as energy performance of all systems. Along with high efficiency components is significant factor related to systems control. Present status of regulation and control systems allow incorporation of highly sophistic procedures. One of these procedures is utilization of artificial neural networks as computational core of the control system. These artificial neural networks offer possibility of approximation of large scale and complex mathematical models. So much simpler approach in mutual relationships between inputs, parameters and outputs replace original model. Another great advantage lies in learning ability from previous results and supplied data. These possibilities discusses following paper." . "ARTIFICIAL NEURAL NETWORKS UTILIZATION WITHIN BUILDING ENERGY AND ENVIRONMENTAL SYSTEMS"@en . "ARTIFICIAL NEURAL NETWORKS UTILIZATION WITHIN BUILDING ENERGY AND ENVIRONMENTAL SYSTEMS" . "Urban, Miroslav" . "RIV/68407700:21110/09:01155148" . "21110" . "3"^^ . . "3"^^ . "Kabele, Karel" . "Information technology progress influences all of our life. Modern buildings are significant example of formation of indoor climate conditions including all HVAC systems within a building. As our qualitative requirements increase, grows also importance of building energy performance as well as energy performance of all systems. Along with high efficiency components is significant factor related to systems control. Present status of regulation and control systems allow incorporation of highly sophistic procedures. One of these procedures is utilization of artificial neural networks as computational core of the control system. These artificial neural networks offer possibility of approximation of large scale and complex mathematical models. So much simpler approach in mutual relationships between inputs, parameters and outputs replace original model. Another great advantage lies in learning ability from previous results and supplied data. These possibilities discusses following paper."@en . "Akademick\u00E9 nakladatelstv\u00ED CERM" . "RIV/68407700:21110/09:01155148!RIV09-MSM-21110___" . "ANN; artificial neural network; HVAC control, building services"@en . . . "Adamovsk\u00FD, Daniel" . "4"^^ . . . . . "VYU\u017DIT\u00CD NEURONOV\u00DDCH S\u00CDT\u00CD V SYST\u00C9MECH TZB"@cs . . "Brno" . "Brno" . . "VYU\u017DIT\u00CD NEURONOV\u00DDCH S\u00CDT\u00CD V SYST\u00C9MECH TZB"@cs . . . .