Supporting method and system for process operation

An apparatus for supporting an operation of a process, comprising:

• a neural circuit model of a hierarchical structure constructed of an input layer, at least one hidden layer, an output layer and a teacher layer;
• and
• a control device for determining a control value of a control variable for a target, to be controlled, in accordance with values of time-dependent input variables so as to bring the target closer to a desired state,
• said control device causing the neural circuit model to learn, out of information on a past operation history of the process, a typical pattern of values of input variables at different points in time as input signals and a value of the control variable, the control value corresponding to the typical pattern, as teacher signal, said values of input variables at different points in time being simultaneously applied to said input layer while said value of the control variable being applied to said teacher layer so that the neural circuit model generates, after the learning, the value of the control variable from said output layer in response to said typical pattern of values of input variables given to said input layer, and
• said control device inputting as the values of the input variables, an unlearned pattern to the thus-learned neural circuit model, to determine its corresponding value of the control variables.

An apparatus for supporting an operation of a process, comprising:

• a neural circuit model of a hierarchical structure constructed of an input layer, at least one hidden layer, and an output layer and a teacher layer;
• and
• a control device for determining a value of a control variable for at least one target to be controlled in accordance with values of time-dependent input variable such that the target can be brought into a desired state,
• said control device causing the neural circuit model to successively learn a plurality of patterns of values of input variables, each by using, as input signals, patterns of values of the input variables at different times of attainment of control of the target into the desired state, and as teacher signals values of the control variables, said values corresponding to the patterns,
• wherein in the learning of one pattern of the values of input variables said values of input variables at different times are simultaneously applied to said input layer while a corresponding value of the control variable are applied to said teacher layer so that the neural circuit model generates, after the successive learning, a desirable value of the control variable from said output layer in response to an unlearned input pattern of values of input variables given to said input layer, and
• said control device inputting, as the values of the input variables, a given pattern of values of the input variables to the thus-learned neural circuit model to determine a value of the control variable for said given pattern.

An apparatus for supporting an operation of a process, comprising:

• a neural circuit model, having a hierarchical structure constructed of an input layer, at least one hidden layer, an output layer and a teacher layer, for supporting an operation of a process;
• and
• a control device for determining a value of a control variable for a target to be controlled in accordance with values of time-dependent input variables so as to bring the target closer to a desired state,
• said control device causing the neural circuit model to learn plural input signals by simultaneously using, as the input signals, at least two patterns of a pattern of values of the input variables at a given time point, a pattern values of the input variables at a time point a predetermined time interval before the given time point and a pattern of differences between the values of the input variables at the given time point and those at the time point a predetermined time interval before the given time point and using a corresponding value for the control variable at the given time point as a teacher signal,
• wherein in the learning of the plural input signals said at least two patterns are simultaneously applied to said input layer while the corresponding value of the control variable are applied to said teacher layer so that the neural circuit model generates, after the learning, a desirable value of the control variable from the output layer in response to unlearned input signals of said at least two patterns given to said input layer, and
• said control device inputting patterns at a present time point simultaneously to the thus-learned neural circuit model to determine a value of the control variable for the present time said patterns corresponding to said at least two patterns.

An apparatus for supporting an operation of a process, comprising:

• a neural circuit model of a hierarchical structure constructed of an input layer, at least one hidden layer, an output layer and a teacher layer;
• a transcendental model adapted to output a target value of a control quantity on the basis on an observation quantity of the process;
• and
• a control device for calculating the difference as an error between an output value from the transcendental model and an actually measured value of the control quantity,
• said control device inputting observation quantities at plural time points having a certain time interval in the past simultaneously to the input layer of the neural circuit model and inputting a corresponding error to the teacher layer of the neural circuit model, learning by the neural circuit model a relationship between the observed quantities and the error so that the neural circuit model generates, after the learning, the corresponding error from said output layer in response to the learned observation quantities given to said input layer, and
• said control device inputting observation quantities at desired time points having said time interval to the thus-learned neural circuit model to have the neural circuit model predict an error at the corresponding time point, and outputting a target value of the control quantity on the basis of the output value from the transcendental model and the error predicted by the learned neural circuit model.

An apparatus for supporting an operation of a process, comprising:

• a neural circuit model of a hierarchical structure constructed of an input layer, at least one hidden layer, an output layer and a teacher layer;
• a transcendental model having a parameter adjustable to output a target value of a control quantity on the basis of an observed quantity of the process;
• and
• a control device for inputting observation quantities at plural time points having certain time interval in the past simultaneously to the input layer of the neural circuit model and inputting a value for the adjustable parameter of the transcendental model at a corresponding time point to the teacher layer of the neural circuit model,
• said control device causing the neural circuit model to learn a relationship between the observed quantities and the value of the parameter so that the neural circuit model generates, after the learning, the value of the parameter from said output layer in response to the learned observation quantities given to said input layer;
• and
• said control device inputting observation quantities at desired time points having said time interval to the thus-learned neural circuit model to have the neural circuit model predict a value of the parameter at the corresponding time point, and obtaining an output value of the transcendental model from the learned neural circuit model by using the thus-predicted value of the parameter and then outputting a target value of the control quantity on the basis of the thus-obtained output value of the transcendental model.