Such methods perform better in large parameter tuning ranges as compared to local optimization methods (pattern search and simulation annealing). For this example, tune the FIS using the particle swarm optimization method ('particleswarm'). To learn new rules, set the OptimizationType to 'learning...
-t It displays the output in tabular form instead of histogram -T It prints the numbers of changes and histogram -u It suppresses the sorting of the filenames -v It shows progress -w It specifies the maximum width of the histogram (the width ranges from 10 to 80)Understand...
The foldmethod option is set to "diff", which puts ranges of lines without changes in a fold. foldcolumn is set to two to make it easy to spot the folds and open or close them. OPTIONS Vertical splits are used to align the lines, as if the "-O" argument was used...
Information in this section Configuring TargetLink tl_global_options To return the settings of global TargetLink options.tl_get_config_path To get a search path for configuration files and hook functions.tl_get_project To return the name of the current TargetLink project as a string.tl_pref To ...
This FIS has the same input and output ranges as the fourth FIS. Get fis5 = fis4; fis5.Name = "fis5"; fis5.Outputs(1).Name = "mpg"; Create FIS Tree Connect the fuzzy systems (fis1, fis2, fis3, fis4, and fis5) according to the FIS tree diagram. Get fisTin = fistree...
tuning process as compared to Mamdani systems. Each FIS includes two inputs and one output, where each input contains two default triangular membership functions (MFs), and the output includes 4 default constant MFs. Specify the input and output ranges using the corresponding data attribute ranges...
Regular expressions (wildcards) may be used to match multiple members: TagDescription * matches a sequence of 0 or more characters ? matches exactly 1 character [...] matches any single character found inside the brackets; ranges are specified by a beginning character, a hyphen, and an ...