RTCP Machine Learning

ADVANTAGES OF THE NEW RTCP MACHINE LEARNING SYSTEM:

• The Controller Peripheral does not need to know the pressure measurement at the Critical Point in order to set regulation correctly.
• This means there is no longer any need for a communication channel between the two devices, and all related issues are therefore eliminated at source (e.g. to do with point to point communication, energy consumption).
• The M.L. Algorithm protects against partial loss of data from one or both Peripherals
• It is evolutionary and adapts daily to new data
• It is analytical and recognises and filters faults in the data
• It is verifiable and offers the operator all the tools to monitor the performance and degree of reliability of forecasting
• It is controllable and allows the operator to define the level of autonomy of the system (control over sending of the Forecast Function to the Controller)
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The New RTCP MACHINE LEARNING System consists of three subjects:

• Peripheral Controller: active subject, forecasts the pressure at the Critical Point and applies it for Regulation.
• Data Logger peripheral: passive subject
• Centre-side Machine Learning algorithm: active subject. Learns the behaviour of the network and models the Controller peripheral.