Method for sensing closing time of injector using artificial neural network and method for controlling injector using the same
A method for sensing a closing time of an injector using an artificial neural network may include: sensing, by a controller, a voltage generated by an injector; performing, by the controller, a preprocess to derive an input matrix using variation characteristics of the voltage; and performing, by the controller, a closing time prediction to derive a closing time of the injector by an artificial neural network model including an input layer including the input matrix, a hidden layer, and an output layer.
1. A method for sensing a closing time of an injector using an artificial neural network, the method comprising:
sensing, by a controller, a voltage generated by an injector;
performing, by the controller, a preprocess to derive an input matrix using variation characteristics of the voltage; and
performing, by the controller, a closing time prediction to derive a closing time of the injector by an artificial neural network model including an input layer including the input matrix, a hidden layer, and an output layer,
wherein the variation characteristics of the voltage are a half-life constant of the voltage, and the half-life constant is a value calculated as:
k
=
-
t
log
2
V
t
V
0
where, V t is a voltage value V for each measurement point, V 0 is a voltage value at an initial measurement point, k is a half-life constant, and t is a time at a measurement point.
2. The method of claim 1 , wherein performing the preprocess comprises:
calculating half-life constants at a plurality of measurement time points of the voltage in a specific section;
deriving an approximation polynomial for changes in the calculated half-life constants in accordance with the time; and
deriving the input matrix by normalizing respective coefficients of the approximation polynomial.
3. The method of claim 2 , wherein in deriving the approximation polynomial, the coefficients of the approximation polynomial are derived using a normal equation utilizing linear algebra.
4. The method of claim 2 , wherein in performing the closing time prediction, the hidden layer derives a first preparation matrix by multiplying the normalized input matrix by a first weight matrix and adding a first bias matrix to the multiplied matrix, and
the hidden layer derives a first resulting matrix by substituting a transfer function of the following equation for the first preparation matrix,
a
1
=
2
1
+
e
-
2
n
1
-
1
where, a 1 is the first resulting matrix, and n 1 is the first preparation matrix.
5. The method of claim 4 , wherein in performing the closing time prediction, the output layer calculates a normalized closing time of the injector by multiplying the first resulting matrix by a second weight matrix and adding a bias value to the multiplied matrix, and the output layer calculates a final closing time of the injector by de-normalizing the calculated closing time of the injector.