Fixed-point virtual sensor control system and method

1. A computer system for converting a virtual sensor model to fixed-point representation used by a control system of a machine, comprising:

a database configured to store information relevant to the virtual sensor model; and

a processor configured to:

establish the virtual sensor model indicative of interrelationships between at least one sensing parameter and a plurality of measured parameters related to the machine;

obtain data and function information representing the virtual sensor model;

convert the data information into fixed-point representation;

convert the function information into fixed-point representation; and

load the converted fixed-point representation of data information and function information in the control system such that the control system uses the virtual sensor model in fixed-point arithmetic operation to control the machine.

2. The computer system according to claim 1 , wherein:

the virtual sensor model is a neural network virtual sensor model; and

the virtual sensor model includes a plurality of neural network layers, each of which includes one or more neural node.

3. The computer system according to claim 2 , wherein:

the neural node includes a weight for a connection between the neural node and a different neural node, and an activation function; and

the data information includes at least the weight and a gain and offset of the activation function.

4. The computer system according to claim 3 , wherein, to convert the data information, the processor is configured to:

determine a fixed-point data type for each number included in the data information based on a magnitude and precision of each number; and

convert each number into a fixed-point number according the respective fixed-point data type.

5. The computer system according to claim 2 , wherein:

the neural node includes an activation function; and

the function information includes a type, input and output relationship, and input and output ranges of the activation function.

6. The computer system according to claim 5 , wherein, to convert the function information, the processor is configured to:

obtain the input range of the activation function;

generate a fixed-point function map representing the input and output relationship of the activation function;

determine the output range of the activation function; and

determine at least one fixed-point data type corresponding to the output range.

7. The computer system according to claim 6 , wherein the function type of the activation function is one of an identity function, an exponential function, a hyperbolic tangent function, and a sigmoidal function.

8. The computer system according to claim 6 , wherein:

the function type of the activation function is an exponential function; and

the at least one fixed-point data type includes a plurality of fixed-point data types each corresponding to a respective part of the output range determined by the input range and the fixed-point function map.

9. A method for a control system of a machine, comprising:

establishing a virtual sensor model indicative of interrelationships between at least one sensing parameter and a plurality of measured parameters related to the machine;

obtaining data and function information representing the virtual sensor model;

converting the data information into fixed-point representation;

converting the function information into fixed-point representation; and

loading the converted fixed-point representation of data information and function information in the control system such that the control system uses the virtual sensor model in fixed-point arithmetic operation to control the machine.

10. The method according to claim 1 , wherein:

the virtual sensor model is a neural network virtual sensor model; and

the virtual sensor model includes a plurality of neural network layers, each of which includes one or more neural node.

11. The method according to claim 10 , wherein:

the neural node includes at least one weight for a connection between the neural node and a different neural node, and an activation function; and

the data information includes at least the weight and a gain and offset of the activation function.

12. The method according to claim 11 , wherein converting the data information includes:

determining a fixed-point data type for each number included in the data information based on a magnitude and precision of each number; and

converting each number into a fixed-point number according the respective fixed-point data type.

13. The method according to claim 10 , wherein:

the neural node includes an activation function; and

the function information includes a type, input and output relationship, and input and output ranges of the activation function.

14. The method according to claim 13 , wherein converting the function information include:

obtaining the input range of the activation function;

generating a fixed-point function map representing the input and output relationship of the activation function;

determining the output range of the activation function; and

determining at least one fixed-point data type corresponding to the output range.

15. The method according to claim 14 , wherein the function type of the activation function is one of an identity function, an exponential function, a hyperbolic tangent function, and a sigmoidal function.

16. The method according to claim 14 , wherein:

the function type of the activation function is an exponential function; and

the at least one fixed-point data type includes a plurality of fixed-point data types each corresponding to a respective part of the output range determined by the input range and the fixed-point function map.

17. The method according to claim 10 , further including:

obtaining values of the plurality of measured parameters;

providing the obtained values to the virtual sensor model; and

obtaining fixed-point value of the at least one measuring parameter from the virtual sensor model through fixed-point arithmetic operation.

18. The method according to claim 17 , wherein:

the at least one measuring parameter includes one of a NO x emission level, a soot emission level, and an HC emission level; and

the plurality measured parameters include at least engine speed, fuel rate, injection timing, intake manifold temperature, intake manifold pressure, inlet valve actuation end of current, and injection pressure.