A method of generating the knowledge base used for a programmable fuzzy controller comprising the steps of determining the relevant input and output variables to be controlled; creating artificial potential fields for each of said variables; sampling each of said potential fields in order to generate fuzzy membership functions; compiling said fuzzy membership functions into fuzzy sets; and mapping inputs fuzzy set to output fuzzy sets through a rule base. The relevant input and output variables are including: minimum, maximum, and equilibrium values; an importance weight; a non-linearity value; a control direction; and information as to whether said variable is an input or output variable. Further provided is a programmable fuzzy controller whose fuzzy knowledge base is obtained by the method described.

A correction control method includes steps of rebuilding a fuzzy inference system, setting a maximum correction value and a minimum correction value, determining whether there is an output value to be outputted, deciding a intermediate correction value and determining whether a range of an interval between the maximum correction value and the intermediate correction value is enough to constitute the fuzzy interval of the membership function, allowing the correction unit to output the maximum correction value, the minimum correction value and the intermediate correction value, adjusting the fuzzy inference system, allowing the fuzzification unit to constitute the minimum correction value according to the output value, determining whether a range of an interval between the maximum correction value and the minimum correction value is enough to constitute the fuzzy interval, and notifying that functions of the fuzzy inference system are failed. The functionality of supervised algorithm of hidden switch is enhanced.

The present invention provides a method for an optimal scheduling decision of an air compressor group based on a simulation technology, which belongs to the technical field of information. The present invention uses expert experience to construct an air compressor energy consumption model sample set, and applies a least squares algorithm to learn relevant parameters of an air compressor energy consumption model; uses maximum energy conversion efficiency and minimum economic cost based on an equivalent electricity as target functions, and applies the simulation technology and a depth first tree search algorithm to solve a multi-target optimal scheduling model of the air compressor group; and finally uses a fuzzy logic theory to describe the preferences of decision makers, and introduces the decision maker preference information into interactive decision making, thereby assisting production staff to formulate safe, economical, efficient and environmentally friendly operation schemes to achieve an operation mode of maximum resource utilization of the air compressor group. The method also has wide application value in different industrial fields.

An action control method and apparatus related to the field of artificial intelligence, where the method includes obtaining states of N dimensions of an artificial intelligence device, obtaining a plurality of discrete decisions based on an active fuzzy subset and a control model that are of a state of each of the N dimensions, where an active fuzzy subset of a state is a fuzzy subset whose membership degree of the state is not zero, the membership degree is used to indicate a degree that the state belongs to the fuzzy subset, performing, based on a membership degree between a state and an active fuzzy subset that are of each dimension, weighted summation on the plurality of discrete decisions, to obtain a continuous decision, and controlling, based on the continuous decision, the artificial intelligence device to execute a corresponding action.

Fuzzy control systems and methods are disclosed. A method includes receiving a reference signal defining target values for a parameter that is controlled at an output of the plasma processing system and obtaining a measure of the parameter that is controlled at the output. A fuzzy controller provides a control signal to adjust at least one actuator based at least upon the reference signal and the measure of the controlled parameter. In addition, output membership functions of the fuzzy controller, input membership functions of the fuzzy controller, and a rule base of the fuzzy controller are adapted while controlling an output of a system based at least upon the based at least upon an estimated model parameter tensor, the reference signal and the measure of the controlled parameter, and the control signal.

Disclosed is a temperature control method which includes acquiring temperature data of a plurality of temperature detection points in a target environment; calculating, according to the temperature data, an average temperature value of the plurality of temperature detection points and a first temperature difference between the average temperature value and a target temperature value; determining whether an absolute value of the first temperature difference exceeds a first temperature difference threshold; and in response to the absolute value of the first temperature difference exceeding the first temperature difference threshold, controlling the temperature of the target environment by a variable universe fuzzy proportional integral derivative control algorithm.

Methods and apparatus for providing a comprehensive decision support system to include predictions, recommendations with consequences and optimal follow-up actions in specific situations are described. Data is obtained from multiple disparate data sources, depending on the information deemed necessary for the situation being modeled. The decision support system provides a prediction or predictions and a recommendation or a choice of recommendations based on the correlative analysis and/or other analyses. Also described are methods and apparatus for developing application specific decision support models. The decision support model development process may include identifying multiple disparate data sources for retrieval of related information, selection of classification variables to be retrieved from the data sources, assignment of weights to each classification variable, selecting and/or defining rules, and selecting and/or defining analysis functions.

A system for detecting and responding to an anomaly in a chaotic environment, comprising one or more autonomous agent devices and a central server comprising a processor and non-transitory memory. The memory stores instructions that cause the processor to receive a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording pseudo-Brownian change in one or more variables in the chaotic environment; determine, based on the first set of sensor readings, an expected range of the one or more variables during a second time window after the first time window; receive a second set of sensor readings from the one or more remote electronic sensors during the second time window recording change in the one or more variables; determine, based on the second set of sensor readings, that one variable of the one or more variables is not within the expected range; and cause the one or more autonomous agent devices to attempt to mitigate a potential harm indicated by the one variable being outside of the expected range.

An artificial-intelligence system for manipulating resources to minimize exposure harm in a chaotic environment, comprising autonomous agent devices, remote electronic sensors, and a central server. The central server receives a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording values of one or more variables in the chaotic environment; receives a critical time interval during which the chaotic environment may affect one or more of the resources and a maximum permitted risk exposure for the time interval; determines a weighted total risk exposure during the critical time interval from the chaotic environment and the resources within the chaotic environment; determines that the weighted total risk exposure exceeds the maximum permitted risk exposure; and causes the autonomous agent devices to manipulate the one or more resources to decrease the weighted total risk exposure.

According to some embodiments, system and methods are provided, comprising receiving, at a machine health module, non-parametric data associated with operation of an installed product; generating, via the machine health module, a health status for at least one of a failure type and a remaining useful life of the installed product, based on the received non-parametric data; and generating an operating response of the installed product based on the generated health status. Numerous other aspects are provided.