Method of generating fuzzy knowledge base for a programmable fuzzy controller

A non-transitory computer readable medium, storing program instructions for execution by a processor, wherein the processor executes the program instructions to perform a method generating a fuzzy knowledge base for a programmable fuzzy controller used to control a process without the use of a database of historical values of relevant input variables, the method comprising the steps of:

• receiving values of relevant input variables and output variables, including non-linearity values indicating linearity degree, to be controlled from a user without the use of a database of historical values of relevant input variables;
• creating artificial potential fields for each of said relevant input variables and output variables;
• sampling each of said potential fields and automatically generating fuzzy membership functions from such sampling;
• choosing a number of membership functions from the generated fuzzy membership functions to provide a linearity degree received from the user;
• compiling the chosen fuzzy membership functions into fuzzy sets;
• and
• mapping the fuzzy sets from relevant input variables to output variables to automatically create fuzzy rules, said fuzzy rules forming the fuzzy knowledge base.

A computer for generating a fuzzy knowledge base for a programmable fuzzy controller used to control a process without the use of a database of historical values of relevant input variables, the computer comprising:

• a processor;
• an input device coupled to said processor for receiving inputs from a user;
• memory coupled to said processor;
• storage coupled to said processor storing program instructions for execution by said processor, wherein said processor executes said program instructions to perform a method generating a fuzzy knowledge base for a programmable fuzzy controller, the method comprising the steps of:
• receiving values of relevant input variables and output variables, including non-linearity values indicating linearity degree, to be controlled from a user without the use of a database of historical values of relevant input variables;
• creating artificial potential fields for each of said relevant input variables and output variables;
• sampling each of said potential fields and automatically generating fuzzy membership functions from such sampling;
• choosing a number of membership functions from the generated fuzzy membership functions to provide a linearity degree received from the user;
• compiling the chosen fuzzy membership functions into fuzzy sets;
• and
• mapping the fuzzy sets from relevant input variables to output variables to automatically create fuzzy rules, said fuzzy rules forming the fuzzy knowledge base.

A programmable fuzzy controller for controlling a process without the use of a database of historical values of relevant input variables, the controller comprising:

• an analog-to-digital convertor for connection to analog input signals of a process to be controlled;
• a digital-to-analog convertor for providing analog output signals to control the process;
• a processor coupled to said analog-to-digital convertor and said digital-to-analog convertor;
• an input device coupled to said processor for receiving inputs from a user;
• memory coupled to said processor;
• storage coupled to said processor storing program instructions for execution by said processor, wherein said processor executes said program instructions to perform a method generating a fuzzy knowledge base for a programmable fuzzy controller and controlling a process using the fuzzy knowledge base, the method comprising the steps of:
• receiving values of relevant input variables and output variables, including non-linearity values indicating linearity degree, to be controlled from a user without the use of a database of historical values of relevant input variables;
• creating artificial potential fields for each of said relevant input variables and output variables;
• sampling each of said potential fields and automatically generating fuzzy membership functions from such sampling;
• choosing a number of membership functions from the generated fuzzy membership functions to provide a linearity degree received from the user;
• compiling the chosen fuzzy membership functions into fuzzy sets;
• mapping the fuzzy sets from relevant input variables to output variables to automatically create fuzzy rules, said fuzzy rules forming the fuzzy knowledge base;
• receiving input values of the process to be controlled from said analog-to-digital convertor;
• and
• operating on the received input values using said fuzzy knowledge base to provide an output to said digital-to-analog convertor for controlling the process.