Abstract: A control apparatus of an electrical appliance comprising a sensor for detecting a physical amount; and a fuzzy inferring device for determining the drive condition of a load by fuzzy inference based on a signal outputted from the sensor, and wherein the sensor has at least one normalized membership function to be used by fuzzy inference and achieves a plurality of membership functions which can be expressed by congruent curves by performing a predetermined subtraction or an addition on the signal outputted from the sensor.

Abstract: An inference system is disclosed, the inference system comprising rule storing portion for storing a plurality of production rules, each of which is composed of an "if statement" and a "then statement", fact information storing portion for storing fact information, condition comparing portion for comparing the "if statement" of each production rule stores in the rule storing portion with the fact information stored in the fact information storing portion successively until a resource of the process system or the inference system is consumed, conflict resolving portion for selecting one of at least one production rule obtained as the results of the comparison by the condition comparing portion when the resource is consumed, and rule executing portion for executing the "then statement" of the production rule being selected by the conflict resolving.

Abstract: To facilitate architecting of a multiple neural network, irrespective of the quantity of cases and the complexity of case dependence relationship, sets of input instances and the desirable outputs corresponding thereto are stored; the stored sets are read in sequence to discriminate whether all variables included in the input instances of the read set are included in the outputs of any given stored set, to mark variables not included in the outputs of any sets; the sets whose input instances include only the marked variables are selected from among the read sets; unit neural networks for learning the selected sets are formed and simultaneously variables included in the outputs of the formed unit neural networks are marked; a unit neural network for learning any given set is formed; and the formed unit neural networks are connected to each other to architect a multiple neural network.