Abstract: A method for storing a membership function, include storing a position of a vertex of a triangle that defines the membership function in a universe of discourse and storing a first distance between the position of the vertex a point of intersection between a left side of the triangle and an axis of the universe of discourse. Further, the method includes storing a second distance between the position of the vertex and point of intersection between right side of the triangle and the axis of the universe of discourse. The present invention furthermore relates to a circuit for calculating a grade of membership of an antecedent of a fuzzy rule, and is adapted to fuzzify an input variable by adopting the geometric proportions that occur between homologous sides of similar triangles defined by the position of the input value in the universe of discourse.

Abstract: A dual-line system of controlling or monitoring processes or industrial plants employing a dual-line fuzzy unit is presented in which due allowance can be made for both positive and negative rules. These are rules issuing "positive recommendations" and "warnings" or "prohibitions" for the selection of the values of the output variables for the fuzzy unit. The dual-line system enables logical compromises to be made between these "recommendations" and "warnings" or "prohibitions". Along with the possibility of drawing on positive empirical knowledge, use of this dual-line system also creates the possibility of utilizing negative empirical knowledge with the same degree of transparency with which positive empirical knowledge has previously been used in conventional fuzzy units. The warnings serve to protect the plant or resources or to prevent undesirable control behavior. The inclusion of warnings or, more especially, prohibitions is also of interest for warranties of operational reliability.

Abstract: A direct adaptive fuzzy controller having an adaptation mechanism that modifies the locations of output membership functions to improve performance of the fuzzy controller. The controller has a fuzzy rule base that has a different or unique output membership function for each fuzzy rule. The adaptation mechanism modifies the location of the output membership functions in response to the performance of the controller system, to continuously improve its performance. The controller is a feedback mechanism that functions not only using measured outputs of the process controlled, but has feedforward compensation that causes the controller to anticipate feedback due to measured disturbances or other parameters of the process.

Abstract: This invention relates to a fuzzy processor having an input X for at least a plurality of input variables X-i and an output U for one or more output results U-k, and including a fuzzyfication unit FU having an input coupled to the input X, a fuzzy rule processing unit RU having an input coupled to the output of the fuzzyfication unit FU, and a defuzzyfication unit DU having an input coupled to the output of the processing unit CU and an output coupled to said output U, wherein the output of the defuzzyfication unit DU is coupled to the input of the fuzzyfication unit FU and/or to the input of the processing unit RU.

Abstract: A fuzzy logic supervisory control architecture for aircraft engines includes a series of fuzzy proportional integral controllers each designed for a specific operating range and a fuzzy logic hierarchical controller for combining the fuel flow derivative outputs of the individual controllers. The hierarchical controller utilizes fuzzy logic on the input space fuzzified to linguistic values for engine temperature, power turbine speed and spool acceleration to determine a weighting to be given to each of the controller outputs for fuel flow derivative.

Abstract: A data processing system (10) selectively weights a fuzzy logic rule in response to a single "REVW" software instruction. In response to the REVW instruction, the data processing system (10) fetches a set of fuzzy inputs associated with the fuzzy logic rule, and determines a minimum fuzzy input from the set. The data processing system (10) then selectively weights the minimum fuzzy input to provide a fuzzy output of the fuzzy logic rule, by multiplying the minimum fuzzy input by a corresponding weight. In one embodiment, a carry bit in a condition code register (60) determines whether the fuzzy logic rule is to be weighted. In response to the single REVW instruction, the data processing system (10) further performs this selective fuzzy rule weighting operation for all rules in a fuzzy rule base.