7. The output parameter estimation apparatus of claim 1, wherein the input data set is composed of a plurality of physical parameter values that are detected in an engine, an intake system, and an exhaust system while the engine is being used, wherein the output parameter value is an air/fuel ratio that is estimated as an actual air/fuel ratio in the engine, the actual air/fuel ratio being unable to be detected by an air/fuel ratio sensor installed in the exhaust system, wherein the neural network is a hierarchical neural network where the plurality of physical parameter values are inputted in the input layer and the estimated air/fuel ratio is outputted from the output layer, and wherein the field division information storage means stores information showing the plurality of fields that are generated by dividing each of at least two physical parameters that include an engine RPM and an intake air pressure into three ranges that are a low-speed range, a medium-speed range, and a high-speed range.
1. 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; fuzzy operation means for calculating a fitness degree of the input data set in each of the determined field and a field adjacent to the determined field by using a fuzzy inference rule for n input parameter values, among the plurality of input parameter values, that correspond to the n input parameters in the n-dimensional space; 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; route selection means for selecting routes between an introduction layer for the neural network and the input layer of the neural network, based on the fitness degrees obtained by the fuzzy operation means; and 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.