Nerdin on MAF
Been quite a while since I was introduced to Gaussian theory in automotive applications, but IIRC, it had to do with predicting outcomes, ala "Fuzzy Logic" computing. However, it's well documented that the MAF voltage signal in the 80 ecu is not a 'learned' value, it's a tabled value. The subsequent fueling correction of LTFT is a learned and retained value, but MAF is not. It only sets the baseline, all fuel'corrections' are after that value is tabled. The timing values have only temp correction after that MAF tabled baseline. Load has no correction after MAF tabled baseline.
As I understand "Guassian neural network" theory you cite, this would be a rather advanced function used in production vehicle testing of values vs faults. For example, if you have a known good MAF voltage plot of 200,000 events of MAF signal being between 1.25 and 4.455 volts, and the actual values 'learned' are .75 to 4.1, Gaussian theory can predict that the outcome of the events learned is the result of a vacuum leak, not a generic OBDII P0102 MAF code (next gen OBD?). It is also applied in acceleration enrichment (AE) profiles, usually as a tabled function of TPS/MAP. Specifically, if you are driving 'sporty-like' Gaussian modeling can change AE values to give more agressive initial timing or more accurate tip in throttle fueling for the predicted ramp change of TPS/MAP.
Unfortunately, that's not a property of the 80 ecu software of the MAF signal. Nor is it common to the MAF signal in most engine ECU's, because it takes a lot of processing power, certainly not found in the 80 ecu.
With regard to the MAF signal temp and age correction, your comments seem to contradict the manufacturers own documents, which claim the function of the trimmer resistor is to account for age and temp. Since the voltage output value is 97% accurate, it seems your Gaussian plot would just be the same as a 1 standard deviation of normal distribution curve in a non-fuzzy logic software application. That's pretty accurate given that 97% accuracy is throughout the measured mass of air of the actual sensor, regardless of pipe diameter.
Straying far from the 80 MAF application, since you have no voltage plot for the stock or the modded MAF sensor to compare or lay claim of any Gaussian theory. And without learned function ability in the MAF signal, there can only be a voltage vs airflow table, with plots dictated by the clock speed of the ecu processor.
The rest of it is cool reading I suppose.
Cheers
Scott J
'94 FZJ80 Supercharged
The modifications made to the wheatstone bridge are not implicitly designed to compensate for aging or contamination since both are expressed with some degree of variability. The bridge is definitely optimized to provide the best transient response of the hot wire for the most common operating parameters, but that's about it.
Here is a great whitepaper on the subject from all the way back in 1991:
https://eprints.kfupm.edu.sa/42430/1/42430.pdf
Though it is a bit dated (back when logic hardware was expensive (predating windows 3.1) the principles of Gaussian neural networks are the same.
Been quite a while since I was introduced to Gaussian theory in automotive applications, but IIRC, it had to do with predicting outcomes, ala "Fuzzy Logic" computing. However, it's well documented that the MAF voltage signal in the 80 ecu is not a 'learned' value, it's a tabled value. The subsequent fueling correction of LTFT is a learned and retained value, but MAF is not. It only sets the baseline, all fuel'corrections' are after that value is tabled. The timing values have only temp correction after that MAF tabled baseline. Load has no correction after MAF tabled baseline.
As I understand "Guassian neural network" theory you cite, this would be a rather advanced function used in production vehicle testing of values vs faults. For example, if you have a known good MAF voltage plot of 200,000 events of MAF signal being between 1.25 and 4.455 volts, and the actual values 'learned' are .75 to 4.1, Gaussian theory can predict that the outcome of the events learned is the result of a vacuum leak, not a generic OBDII P0102 MAF code (next gen OBD?). It is also applied in acceleration enrichment (AE) profiles, usually as a tabled function of TPS/MAP. Specifically, if you are driving 'sporty-like' Gaussian modeling can change AE values to give more agressive initial timing or more accurate tip in throttle fueling for the predicted ramp change of TPS/MAP.
Unfortunately, that's not a property of the 80 ecu software of the MAF signal. Nor is it common to the MAF signal in most engine ECU's, because it takes a lot of processing power, certainly not found in the 80 ecu.
With regard to the MAF signal temp and age correction, your comments seem to contradict the manufacturers own documents, which claim the function of the trimmer resistor is to account for age and temp. Since the voltage output value is 97% accurate, it seems your Gaussian plot would just be the same as a 1 standard deviation of normal distribution curve in a non-fuzzy logic software application. That's pretty accurate given that 97% accuracy is throughout the measured mass of air of the actual sensor, regardless of pipe diameter.
Straying far from the 80 MAF application, since you have no voltage plot for the stock or the modded MAF sensor to compare or lay claim of any Gaussian theory. And without learned function ability in the MAF signal, there can only be a voltage vs airflow table, with plots dictated by the clock speed of the ecu processor.
The rest of it is cool reading I suppose.
Cheers
Scott J
'94 FZJ80 Supercharged