Generic neural network training and processing system

Apparatus for developing a neural network control function performed by an electronic engine control microprocessor coupled to an internal combustion engine, said apparatus comprising, in combination:

• sensing means coupled to said engine for producing a plurality of input signal values, each of which is indicative of a particular engine operation condition,
• data storage means for storing a plurality of neural network definition data structures, each of which includes:
• data defining the values of signals being processed by said given neural network, and
• weighting values governing the manner in which signals are combined within said given neural network,
• program storage means for storing instructions executable by said electronic engine control microprocessor, said instructions including means responsive to the identification of a particular network definition data structure for performing a generic neural network routine for combining at least selected ones of said input signal values to produce and store new signal values in said particular data structure in accordance with said weighting values in said particular data structure,
• a training processor external to and operating independently of said electronic engine control microprocessor, said training processor being coupled to said data storage means and including means for monitoring changes in the values stored in a selected one of said data structures, and means for altering the values of weighting values stored in said selected one of said data structures to alter the new signal values produced within said selected one of said data structures by the operation of said generic neural network routine,
• output means responsive to one or more of said new signal values for generating at least one output signal, and
• actuation means responsive to said output signal for controlling the operation of said engine.

The method of training a neural network to control the operation of an internal combustion engine, said neural network being implemented by an electronic engine control (EEC) processor connected to receive input signal values indicative of the operating state of said engine and being further connected to supply output signals to control the operation of said engine, said method comprising the steps of:

• interconnecting an external training processor to said electronic engine control processor such that said external training processor can access said input signal values,
• generating and storing a data structure consisting of an initial set of neural network weighting values,
• operating a representative internal combustion engine and its connected electronic engine control processor over a range of operating conditions,
• concurrently with the operation of said engine, executing a neural network control program on said electronic engine control processor to process said input signal values into output control values in accordance with the values stored in said data structures,
• concurrently with the operation of said engine, varying said output signals in accordance with said output control values to control the operation of said engine,
• concurrently with the operation of said engine, executing a neural network training program on said external training processor to progressively alter at least selected values in said data structure to modify the results produced during the execution of said neural network training program,
• evaluating the operation of said engine to indicate when a desired operating behavior is achieved, and
• utilizing the values in said data structure at the time said desired operating behavior is achieved to control the execution of said neural network control program on said EEC to control production engines corresponding to said representative engine.