8. An output parameter estimation apparatus for estimating an output parameter value from an input data set that is composed of a plurality of input parameter values, wherein the input data set is obtained whenever time series input data is sampled, the output parameter estimation apparatus comprising: field division information storage means for storing field division information showing how an n-dimensional space is divided into a plurality of fields, "n" being an integer no less than 1, and the n-dimensional space having n axes that correspond to n input parameters; field determination means for determining a field, among the plurality of fields, to which the input data set belongs, according to the field division information; a neural network, including an input layer, an output layer, and at least one hidden layer between the input layer and the output layer, the input layer, the hidden layer, and the output layer being connected according to a connection condition corresponding to each of the plurality of fields, the connection conditions having been set as a result of learning performed using learning data sets; route selection means for selecting routes between an introduction layer for the neural network and the input layer of the neural network in accordance with the field determined by the field determination means; input data introduction means for inputting, after the routes are selected by the route selection means, the input data set into the input layer of the neural network via the introduction layer and the selected routes, wherein, after being inputted in the input layer, the input data set is transferred to the output layer via the hidden layer in the connection condition corresponding to the determined field, and as a result the estimated output parameter value is outputted from the output layer; estimation accuracy judgement means for comparing, each time an input data set obtained by sampling the time series input data is inputted in the input layer by the input data introduction means, an estimated output parameter value outputted from the output layer with a base value corresponding to the estimated output parameter value in order to judge an estimation accuracy of the neural network in a connection condition corresponding to each field; first relearning means for performing learning, when the estimation accuracy is judged as poor for at least one field, on a connection condition of the neural network corresponding to the field using learning data sets in order to obtain a new connection condition, each learning data set being composed of an input data set that belongs to the field and a base value corresponding to the input data set, and the learning being performed after the introduction layer and the input layer are connected by the routes selected by the route selection means; and neural network renewal means for renewing the connection condition of the neural network corresponding to the field based on the new connection condition.
8. An output parameter estimation apparatus for estimating an output parameter value from an input data set that is composed of a plurality of input parameter values, wherein the input data set is obtained whenever time series input data is sampled, the output parameter estimation apparatus comprising: field division information storage means for storing field division information showing how an n-dimensional space is divided into a plurality of fields, "n" being an integer no less than 1, and the n-dimensional space having n axes that correspond to n input parameters; field determination means for determining a field, among the plurality of fields, to which the input data set belongs, according to the field division information; a neural network, including an input layer, an output layer, and at least one hidden layer between the input layer and the output layer, the input layer, the hidden layer, and the output layer being connected according to a connection condition corresponding to each of the plurality of fields, the connection conditions having been set as a result of learning performed using learning data sets; route selection means for selecting routes between an introduction layer for the neural network and the input layer of the neural network in accordance with the field determined by the field determination means; input data introduction means for inputting, after the routes are selected by the route selection means, the input data set into the input layer of the neural network via the introduction layer and the selected routes, wherein, after being inputted in the input layer, the input data set is transferred to the output layer via the hidden layer in the connection condition corresponding to the determined field, and as a result the estimated output parameter value is outputted from the output layer; estimation accuracy judgement means for comparing, each time an input data set obtained by sampling the time series input data is inputted in the input layer by the input data introduction means, an estimated output parameter value outputted from the output layer with a base value corresponding to the estimated output parameter value in order to judge an estimation accuracy of the neural network in a connection condition corresponding to each field; first relearning means for performing learning, when the estimation accuracy is judged as poor for at least one field, on a connection condition of the neural network corresponding to the field using learning data sets in order to obtain a new connection condition, each learning data set being composed of an input data set that belongs to the field and a base value corresponding to the input data set, and the learning being performed after the introduction layer and the input layer are connected by the routes selected by the route selection means; and neural network renewal means for renewing the connection condition of the neural network corresponding to the field based on the new connection condition. 14. The output parameter estimation apparatus of claim 8, further comprising: generalization ability judgement means for comparing, after the connection condition of the neural network is renewed by the neural network renewal means, output parameter values, which are estimated by the neural network respectively from input data sets that belong to the field, with corresponding base values to judge a generalization ability of the neural network in the new connection condition; and renewal cancel means for canceling, when the generalization ability is judged as poor, the new connection condition of the neural network corresponding to the field.