The invention provides a fuzzy curve analysis based soft sensor modeling method using time difference Gaussian process regression, it is suitable for application in chemical process with time delay characteristics. This method can extract stable delay information from the historical database of process and introduce more relevant modeling data sequence to the dominant variable sequence. First of all, the method of fuzzy curve analysis (FCA) can intuitively judge the importance of the input sequence to the output sequence, estimate the time-delay parameters of process, and such offline time-delay parameter set can be utilized to restructure the modeling data. For the new input data, based on the historical variable value before a certain time, the current dominant value can be predicted by time difference Gaussian Process Regression (TDGPR) model. This method does not encounter the problem of model updating and can effectively track the drift between input and output data. Compared with steady-state modeling methods, this invention can achieve more accurate predictions of the key variable, thus improving product quality and reducing production costs.

An electronic control device includes: a flow rate calculation unit that calculates a flow rate of intake air based on an output signal of a flow rate measurement device assembled to an intake pipe; a flow rate correction value calculation unit that calculates an average value, a maximum value, and a minimum value of the flow rate of the intake air, calculated by the flow rate calculation unit, during a predetermined period, and an amplitude of a signal with one or more frequencies equal to or higher than a fundamental frequency of the output signal of the flow rate measurement device and included in the output signal of the flow rate measurement device, and calculates a correction value for the flow rate of the intake air based on calculation results; and a flow rate correction unit that corrects the flow rate of the intake air based on the correction value.

The invention relates to an apparatus and a method for controlling 4 a process within a system, particularly a combustion process in a boiler or furnace, comprising the following steps: capturing 1 of state variables (s.sub.t) of the system; creating 2 an interference model, which describes the effects of interference-based system changes (v.sub.t) on the state variables (s.sub.t) of the system; creating 3 a process model, which describes the effects of setting actions (a.sub.t) on the state variables (s.sub.t) of the system; and controlling 4 the process within the system by performing setting actions (a.sub.t) by considering the process model, the interference model and predetermined controlling goals.

The invention provides a fuzzy curve analysis based soft sensor modeling method using time difference Gaussian process regression, it is suitable for application in chemical process with time delay characteristics. This method can extract stable delay information from the historical database of process and introduce more relevant modeling data sequence to the dominant variable sequence. First of all, the method of fuzzy curve analysis (FCA) can intuitively judge the importance of the input sequence to the output sequence, estimate the time-delay parameters of process, and such offline time-delay parameter set can be utilized to restructure the modeling data. For the new input data, based on the historical variable value before a certain time, the current dominant value can be predicted by time difference Gaussian Process Regression (TDGPR) model. This method does not encounter the problem of model updating and can effectively track the drift between input and output data. Compared with steady-state modeling methods, this invention can achieve more accurate predictions of the key variable, thus improving product quality and reducing production costs.

Technologies are provided for applying energy to a combustion reaction. For example, a method may include supporting a combustion reaction; applying energy to the combustion reaction via one or more control signals; detecting a change in one or more parameters associated with the combustion reaction; comparing the change in the one or more parameters to a database; determining whether the change in the one or more parameters corresponds to a change in the combustion reaction; selecting a change in the one or more control signals from the database; and applying the change in the one or more control signals to change the a value of the energy applied to the combustion reaction responsive to changes in the one or more parameters associated with in the combustion reaction.

The invention relates to an apparatus and a method for controlling 4 a process within a system, particularly a combustion process in a boiler or furnace, comprising the following steps: capturing 1 of state variables (s.sub.t) of the system; creating 2 an interference model, which describes the effects of interference-based system changes (v.sub.t) on the state variables (s.sub.t) of the system; creating 3 a process model, which describes the effects of setting actions (a.sub.t) on the state variables (s.sub.t) of the system; and controlling 4 the process within the system by performing setting actions (a.sub.t) by considering the process model, the interference model and predetermined controlling goals.

The invention provides a fuzzy curve analysis based soft sensor modeling method using time difference Gaussian process regression, it is suitable for application in chemical process with time delay characteristics. This method can extract stable delay information from the historical database of process and introduce more relevant modeling data sequence to the dominant variable sequence. First of all, the method of fuzzy curve analysis (FCA) can intuitively judge the importance of the input sequence to the output sequence, estimate the time-delay parameters of process, and such offline time-delay parameter set can be utilized to restructure the modeling data. For the new input data, based on the historical variable value before a certain time, the current dominant value can be predicted by time difference Gaussian Process Regression (TDGPR) model. This method does not encounter the problem of model updating and can effectively track the drift between input and output data. Compared with steady-state modeling methods, this invention can achieve more accurate predictions of the key variable, thus improving product quality and reducing production costs.

An electronic control device includes: a flow rate calculation unit that calculates a flow rate of intake air based on an output signal of a flow rate measurement device assembled to an intake pipe; a flow rate correction value calculation unit that calculates an average value, a maximum value, and a minimum value of the flow rate of the intake air, calculated by the flow rate calculation unit, during a predetermined period, and an amplitude of a signal with one or more frequencies equal to or higher than a fundamental frequency of the output signal of the flow rate measurement device and included in the output signal of the flow rate measurement device, and calculates a correction value for the flow rate of the intake air based on calculation results; and a flow rate correction unit that corrects the flow rate of the intake air based on the correction value.