Method and apparatus for parameter estimation, parameter estimation control and learning control

A parameter estimation device for estimating parameters relating to input and output of a control object by using a neural network, comprising:

• control domain division means for selecting at least one parameter which has strong correlation with non-linearity amongst input-output characteristics of the control object, as a parameter of a premise part of fuzzy operation, and dividing a control domain of the control object into a plurality of small domains by fuzzy estimation based on the parameter of the premise part;
• and
• estimation means comprising a map formation unit and a map composition unit, and estimating dynamic behavior of the control object by using map values output from the map combination unit;
• said map formation unit performing as follows:
• dividing plural parameters indicating the operation states of the respective small control domains into a predetermined number of parameter groups to be input to each of plural neural networks;
• dividing output parameters from each of the plural neural networks into a predetermined number of parameter groups;
• constructing, as a consequent part of the fuzzy operation, neural networks receiving these parameter groups;
• performing learning of a neural network construction obtained by combining plural neural networks which are obtained by repeating the above operation until the output from each neural network becomes single;
• and forming maps by using the outputs from the respective neural networks corresponding to the respective input parameter groups;
• and
• said map combination unit combining the maps so formed in the same manner as combining the neural networks.

A parameter estimation control method for estimating parameters relating to control of a control object by using a neural network, and controlling the control object according to the estimated parameters, comprising the steps of:

• making a neural network learn by using the result of detection from a predetermined sensor means as a teaching signal;
• generating an estimate value of the output of the control object in accordance with a state quantity indicating the state of the control object, by the neural network which has learned;
• and
• generating a control quantity used for control of the control object, based on the generated estimate value, according to a variation adaptive operation process which is adaptive to a variation of the estimate value.

A learning control device comprising:

• a neural network for receiving a plurality of input parameter values relating to a parameter as an object of estimation, and estimating estimation object parameter values used for target follow-up control quantity operation from these input parameter values;
• error coefficient change means for changing values of weight coefficients by which a square error between a neuro estimate value output from the neural network and a teaching signal is to be multiplied, according to relationships among the neuro estimate value, the teaching signal, and a target value;
• and
• performance function operation means for operating a performance function for learning by using the weight coefficients;
• wherein learning of the neural network is performed based on the performance function.

A learning control method receiving a plurality of input parameter values relating to a parameter as an object of estimation, and performing learning control of a neural network which estimates estimation object parameter values used for target follow-up control quantity operation from those input parameter values, said method comprising the steps of:

• changing values of weight coefficients by which a square error between a neuro estimate value output from the neural network and a teaching signal is to be multiplied, according to relationships among the neuro estimate value, the teaching signal, and a target value;
• operating a performance function for learning by using the weight coefficients;;
• and
• performing learning of the neural network based on the performance function.

A learning control device comprising:

• a neural network for receiving a plurality of input parameter values relating to a parameter as an object of estimation, and estimating estimation object parameter values used for target follow-up control quantity operation from these input parameter values;
• estimation error sign decision means for deciding the kind of a sign of an error between a neuro estimate value output from the neural network and a target value;
• teaching error sign decision means for deciding the kind of a sign of an error between a teaching signal and the target value;
• sign comparison means for comparing the kinds of the respective signs decided by the estimation error sign decision means and the teaching error sign decision means;
• square error coefficient change means for increasing values of weight coefficients by which a square error between the neuro estimate value and the teaching signal is to be multiplied, when the sign comparison means decides that the signs are different from each other;
• and
• performance function operation means for operating a performance function for learning by using the weight coefficients;
• wherein learning of the neural network is performed based on the performance function.

A learning control device comprising:

• a neural network for receiving a plurality of input parameter values relating to a parameter as an object of estimation, and estimating estimation object parameter values used for target follow-up control quantity operation from these input parameter values;
• estimation error sign decision means for deciding the kind of a sign of an error between a neuro estimate value output from the neural network and a target value;
• estimation error absolute value calculation means for calculating an absolute value of the error between the neuro estimate value and the target value;
• teaching error sign decision means for deciding the kind of a sign of an error between a teaching signal and the target value;
• teaching signal error absolute value calculation means for calculating an absolute value of the error between the teaching signal and the target value;
• sign comparison means for comparing the kinds of the respective signs decided by the estimation error sign decision means and the teaching error sign decision means;
• absolute value comparison means for comparing the absolute values calculated by the estimation error absolute value calculation means and the teaching signal error absolute value calculation means;
• square error coefficient change means for increasing values of weight coefficients by which a square error between the neuro estimate value and the teaching signal is to be multiplied, when the sign comparison means decides that the kinds of the signs are identical, and the absolute value comparison means decides that the absolute value of the error between the neuro estimate value and the target value is smaller than the absolute value of the error between the teaching signal and the target value;
• and
• performance function operation means for operating a performance function for learning by using the weight coefficients;
• wherein learning of the neural network is performed based on the performance function.

A learning control device comprising:

• a neural network having a plurality of neuro constructions having their respective learning conditions, which receive a plurality of input parameter values relating to a parameter as an object of estimation, and estimate estimation object parameter values used for target follow-up control quantity operation from these input parameter values;
• state quantity detection means for detecting a state quantity relating to dynamic behavior of a control object;
• learning condition decision means for deciding the learning condition of the neural network in accordance with the detected value;
• and
• neuro selection means for selecting only a neuro output value corresponding to the present condition amongst the neuro constructions of the neural network in accordance with the detected value.

A learning control device comprising:

• a neural network having a plurality of neuro constructions having their respective learning conditions, which receive a plurality of input parameter values relating to a parameter as an object of estimation, and estimate estimation object parameter values used for target follow-up control quantity operation from these input parameter values, said device comprising:
• state quantity detection means for detecting a state quantity relating to dynamic behavior of a control object;
• learning condition decision means for deciding the learning condition of the neural network in accordance with the detected value;
• learning data storage means for storing learning data sets which have learned connection coefficients of the respective neuro constructions;
• learning data formation means for forming learning data for each of the learning conditions in accordance with the state quantities;
• learning means for performing learning of neuro connection coefficients, by using the learning data set corresponding to the condition stored in the learning data storage means, and the learning data newly formed by the learning data formation means;
• and
• coefficient updation/neuro selecting means for selecting a neuro construction corresponding to the condition and updating the neuro construction to the connection coefficients which are the learning result.

A learning control device comprising:

• a neural network having a plurality of neuro constructions having their respective learning conditions, which receive a plurality of input parameter values relating to a parameter as an object of estimation, and estimate estimation object parameter values used for target follow-up control quantity operation from these input parameter values;
• state quantity detection means for detecting a state quantity relating to dynamic behavior of a control object;
• learning condition decision means for deciding the learning condition of the neural network in accordance with the detected value;
• learning data storage means for storing learning data sets which have learned connection coefficients of the respective neuro constructions;
• learning data formation means for forming new learning data for each of the learning conditions in accordance with the state quantity;
• learning data set formation means for forming learning data sets for the respective neuro constructions, by using the learning data sets of all the neuro constructions corresponding to the learning condition stored in the learning data storage means, and the learning data newly formed by the learning data formation means;
• learning data updation means for updating the corresponding data in the learning data storage means to the newly formed learning data sets;
• learning means for performing learning of all the neuro connection coefficients corresponding to the learning condition by using the newly formed learning data sets;
• coefficient updation/neuro selection means for selecting all the neuro constructions and updating them to the connection coefficients which are the learning result;
• neuro construction selection means for selecting all the neuro constructions corresponding to the learning condition;
• and
• estimate calculation means for calculating a neuro estimate value to be used for control, from all the corresponding neuro outputs;
• wherein said estimate value is used for control quantity operation.