| 摘要 |
A multi-kernel neural network computing architecture configured to learn correlations among feature values 34, 38 as the network monitors and imputes measured input values 30 and also predicts future output values 46. This computing architecture, referred to as a concurrent-learning information processor (CIP 10), includes a multi-kernel neural network array 14 with the capability to learn and predict in real time. The CIP 10 also includes a manager 16 and an input-output transducer 12 that may be used for input-output refinement. These components allow the computing capacity of the multi-kernel array 14 to be reassigned in response to measured performance or other factors. The output feature values 46 computed by the multi-kernel array 14 and processed by an output processor 44 of the transducer 12 are supplied to a response unit 18 that may be configured to perform a variety of monitoring, forecasting, and control operations in response to the computed output values. Important characteristics of the CIP 10, such as feature function specifications 35 and 49, connection specifications 42, learning weight schedules 55, and the like may be set by a technician through a graphical user interface 20. Refinement processes also allow the CIP 10 be reconfigured in accordance with user commands for application to different physical applications.
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