# frbs.gen: The frbs model generator

Description Usage Arguments Details Value Examples

### Description

The purpose of this function is to generate a FRBS model from user-given input without a learning process.

### Usage

 1 2 3 4 5 frbs.gen(range.data, num.fvalinput, names.varinput, num.fvaloutput = NULL, varout.mf = NULL, names.varoutput = NULL, rule, varinp.mf, type.model = "MAMDANI", type.defuz = "WAM", type.tnorm = "MIN", type.snorm = "MAX", func.tsk = NULL, colnames.var = NULL, type.implication.func = "ZADEH", name = "Sim-0") 

### Arguments

 range.data a matrix (2 \times n) containing the range of the data, where n is the number of variables, and first and second rows are the minimum and maximum values, respectively. num.fvalinput a matrix representing the number of linguistic terms of each input variables. For example: num.fvalinput <- matrix(c(3,2), nrow = 1) means that there are two variables where the first variable has three linguistic terms and the second one has two linguistic terms. names.varinput a list containing names to the linguistic terms for input variables. See rulebase. num.fvaloutput the number of linguistic terms of the output variable. This parameter is required for the Mamdani model only. For example: num.fvaloutput <- matrix(3, nrow = 1) means there are 3 linguistic terms for the output variable. varout.mf a matrix for constructing the membership functions of the output variable. The form is the same as for the varinp.mf parameter. This parameter is required for the Mamdani model only. See fuzzifier. names.varoutput a list giving names of the linguistic terms for the output variable. The form is the same as for the names.varinput parameter. This parameter is required for the Mamdani model only. See rulebase. rule a list of fuzzy IF-THEN rules. There are some types of rule structures, for example: Mamdani, Takagi Sugeno Kang, and fuzzy rule-based classification systems (FRBCS). If we use the Mamdani model then the consequent part is a linguistic term, but if we use Takagi Sugeno Kang then we build a matrix representing linear equations in the consequent part. e.g., "a1", "and", "b1, "->", "e1" means that "IF inputvar.1 is a1 and inputvar.2 is b1 THEN outputvar.1 is e1". Make sure that each rule has a "->" sign. Furthermore, we are allowed to use linguistic hedges (e.g., "extremely", "slightly", etc), negation (i.e., "not"), and the "dont_care" value representing degree of membership is always 1. For more detail, see rulebase. varinp.mf a matrix for constructing the shapes of the membership functions. See how to construct it in fuzzifier. type.model the type of the model. There are three types available as follows. MAMDANI means we are using the Mamdani model. TSK means we are using the Takagi Sugeno Kang model. FRBCS means we are using fuzzy rule-based classification systems (FRBCS). type.defuz the type of the defuzzification method. It is used in the Mamdani model only. See defuzzifier. type.tnorm the type of the t-norm method. See inference. type.snorm the type of the s-norm method. See inference. func.tsk a matrix of parameters of the function on the consequent part using the Takagi Sugeno Kang model. This parameter must be defined when we are using Takagi Sugeno Kang. See rulebase. colnames.var a list of names of input and output variables. type.implication.func a type of implication function. See WM. name a name of the simulation.

### Details

It can be used if rules have already been obtained manually, without employing the learning process. In the examples shown, we generate a fuzzy model using frbs.gen and generate the fuzzy rule-based systems step by step manually. Additionally, the examples show several scenarios as follows.

• Using frbs.gen for constructing the Mamdani model on a regression task.

• Using frbs.gen for constructing the Takagi Sugeno Kang model on a regression task.

• Constructing the Mamdani model by executing internal functions such as rulebase, fuzzifier, inference, and defuzzifier for the Mamdani model.

• Using frbs.gen for constructing fuzzy rule-based classification systems (FRBCS) model.

### Value

The frbs-object.

### Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 ################################################# ## 1. The following codes show how to generate a fuzzy model ## using the frbs.gen function for regression tasks. ## The following are three scenarios: ## 1a. Using the Mamdani model ## 1b. Using the Takagi Sugeno Kang model ## 1c. Using the Mamdani model and internal functions: fuzzifier, etc. ## Note: ## In the examples, let us consider four input variabels and one output variable. ## Some variables could be shared together for other examples. ################################################# ## Define shape and parameters of membership functions of input variables. ## Please see the fuzzifier function to construct the matrix. ## It can be seen that in this case we employ TRAPEZOID as the membership functions. varinp.mf <- matrix(c(2, 0, 20, 40, NA, 4, 20, 40, 60, 80, 3, 60, 80, 100, NA, 2, 0, 35, 75, NA, 3, 35, 75, 100, NA, 2, 0, 20, 40, NA, 1, 20, 50, 80, NA, 3, 60, 80, 100, NA, 2, 0, 20, 40, NA, 4, 20, 40, 60, 80, 3, 60, 80, 100, NA), nrow = 5, byrow = FALSE) ## Define number of linguistic terms of the input variables. ## Suppose, we have 3, 2, 3, and 3 numbers of linguistic terms ## for the first, second, third and fourth variables, respectively. num.fvalinput <- matrix(c(3, 2, 3, 3), nrow=1) ## Give the names of the linguistic terms of each input variables. varinput.1 <- c("a1", "a2", "a3") varinput.2 <- c("b1", "b2") varinput.3 <- c("c1", "c2", "c3") varinput.4 <- c("d1", "d2", "d3") names.varinput <- c(varinput.1, varinput.2, varinput.3, varinput.4) ## Set interval of data. range.data <- matrix(c(0,100, 0, 100, 0, 100, 0, 100, 0, 100), nrow=2) ## Define inference parameters. type.defuz <- "WAM" type.tnorm <- "MIN" type.snorm <- "MAX" type.implication.func <- "ZADEH" ## Give the name of simulation. name <- "Sim-0" ## Provide new data for testing. newdata <- matrix(c(15, 80, 85, 85, 45, 75, 78, 70), nrow = 2, byrow = TRUE) ## the names of variables colnames.var <- c("input1", "input2", "input3", "input4", "output1") ################################################################### ## 1a. Using the Mamdani Model #################################################################### ## Define number of linguistic terms of output variable. ## In this case, we set the number of linguistic terms to 3. num.fvaloutput <- matrix(c(3), nrow = 1) ## Give the names of the linguistic terms of the output variable. varoutput.1 <- c("e1", "e2", "e3") names.varoutput <- c(varoutput.1) ## Define the shapes and parameters of the membership functions of the output variables. varout.mf <- matrix(c(2, 0, 20, 40, NA, 4, 20, 40, 60, 80, 3, 60, 80, 100, NA), nrow = 5, byrow = FALSE) ## Set type of model which is "MAMDANI" or "TSK" for Mamdani or ## Takagi Sugeno Kang models, respectively. ## In this case, we choose the Mamdani model. type.model <- "MAMDANI" ## Define the fuzzy IF-THEN rules; In this case, we provide two scenarios using different operators: rule.or <- matrix(c("a1", "or", "b1", "or", "c1", "or", "d1", "->", "e1", "a2", "and", "b2", "and", "c2", "and", "d2", "->", "e2", "a3", "and", "b2", "and", "c2", "and", "d1", "->", "e3"), nrow = 3, byrow = TRUE) ## Define the fuzzy IF-THEN rules; rule.and <- matrix(c("a1", "and", "b1", "and", "c1", "and", "d1", "->", "e1", "a2", "and", "b2", "and", "c2", "and", "d2", "->", "e2", "a3", "and", "b2", "and", "c2", "and", "d1", "->", "e3"), nrow = 3, byrow = TRUE) ## Generate a fuzzy model with frbs.gen. object.or <- frbs.gen(range.data, num.fvalinput, names.varinput, num.fvaloutput, varout.mf, names.varoutput, rule.or, varinp.mf, type.model, type.defuz, type.tnorm, type.snorm, func.tsk = NULL, colnames.var, type.implication.func, name) object.and <- frbs.gen(range.data, num.fvalinput, names.varinput, num.fvaloutput, varout.mf, names.varoutput, rule.and, varinp.mf, type.model, type.defuz, type.tnorm, type.snorm, func.tsk = NULL, colnames.var, type.implication.func, name) ## Plot the membership function. plotMF(object.and) ## Predicting using new data. res.or <- predict(object.or, newdata)$predicted.val res.and <- predict(object.and, newdata)$predicted.val ##################################################################### ## 1b. Using the Takagi Sugeno Kang (TSK) Model ##################################################################### ## Define "TSK" for the Takagi Sugeno Kang model type.model <- "TSK" ## Define linear equations for consequent parts. ## The following command means that we have three equation related to the rules we have. ## e.g., the first equation is 1*inputvar.1 + 1*inputvar.2 + 5*inputvar.3 + 2*inputvar.4 + 1, ## where inputvar.i is a value of the i-th input variable. func.tsk <- matrix(c(1, 1, 5, 2, 1, 3, 1, 0.5, 0.1, 2, 1, 3, 2, 2, 2), nrow = 3, byrow = TRUE) ## Define the fuzzy IF-THEN rules; ## For TSK model, it isn't necessary to put linguistic term in consequent parts. ## Make sure that each rule has a "->" sign. rule <- matrix(c("a1", "and", "b1", "and", "c1", "and", "d1", "->", "a2", "and", "b2", "and", "c2", "and", "d2", "->", "a3", "and", "b2", "and", "c2", "and", "d1", "->"), nrow = 3, byrow = TRUE) ## Generate a fuzzy model with frbs.gen. ## It should be noted that for TSK model, we do not need to input: ## num.fvaloutput, varout.mf, names.varoutput, type.defuz. object <- frbs.gen(range.data, num.fvalinput, names.varinput, num.fvaloutput = NULL, varout.mf = NULL, names.varoutput = NULL, rule, varinp.mf, type.model, type.defuz = NULL, type.tnorm, type.snorm, func.tsk, colnames.var, type.implication.func, name) ## Plot the membership function. plotMF(object) ## Predicting using new data. res <- predict(object, newdata)\$predicted.val ###################### ## 1c. Using the same data as in the previous example, this example performs ## step by step of the generation of a fuzzy rule-based system ###################### ## Using the Mamdani model. type.model <- "MAMDANI" ## Construct rules. rule <- matrix(c("a1", "and", "b1", "and", "c1", "and", "d1", "->", "e1", "a2", "and", "b2", "and", "c2", "and", "d2", "->", "e2", "a3", "and", "b2", "and", "c2", "and", "d1", "->", "e3"), nrow = 3, byrow = TRUE) ## Check input data given by user. rule <- rulebase(type.model, rule, func.tsk = NULL) ## Fuzzification Module: ## In this function, we convert crisp into linguistic values/terms ## based on the data and the parameters of the membership function. ## The output: a matrix representing the degree of the membership of the data num.varinput <- ncol(num.fvalinput) MF <- fuzzifier(newdata, num.varinput, num.fvalinput, varinp.mf) ## Inference Module: ## In this function, we will calculate the confidence factor on the antecedent for each rule ## considering t-norm and s-norm. miu.rule <- inference(MF, rule, names.varinput, type.tnorm, type.snorm) ## Defuzzification Module. ## In this function, we calculate and convert the linguistic values back into crisp values. range.output <- range.data[, ncol(range.data), drop = FALSE] result <- defuzzifier(newdata, rule, range.output, names.varoutput, varout.mf, miu.rule, type.defuz, type.model, func.tsk = NULL) ################################################# ## 2. The following codes show how to generate a fuzzy model ## using the frbs.gen function for classification tasks using the Mamdani model. ################################################# ## define range of data. ## Note. we only define range of input data. range.data.input <- matrix(c(0, 1, 0, 1, 0, 1, 0, 1), nrow=2) ## Define shape and parameters of membership functions of input variables. ## Please see fuzzifier function to construct the matrix. ## In this case, we are using TRIANGLE for membership functions. varinp.mf <- matrix(c(1, 0, 0, 0.5, NA, 1, 0, 0.5, 1, NA, 1, 0.5, 1, 1, NA, 1, 0, 0, 0.5, NA, 1, 0, 0.5, 1, NA, 1, 0.5, 1, 1, NA, 1, 0, 0, 0.5, NA, 1, 0, 0.5, 1, NA, 1, 0.5, 1, 1, NA, 1, 0, 0, 0.5, NA, 1, 0, 0.5, 1, NA, 1, 0.5, 1, 1, NA), nrow = 5, byrow = FALSE) ## Define number of linguistic terms of input variables. ## Suppose, we have 3, 3, 3, and 3 numbers of linguistic terms ## for first up to fourth variables, respectively. num.fvalinput <- matrix(c(3, 3, 3, 3), nrow=1) ## Give the names of the linguistic terms of each input variable. varinput.1 <- c("v.1_a.1", "v.1_a.2", "v.1_a.3") varinput.2 <- c("v.2_a.1", "v.2_a.2", "v.2_a.3") varinput.3 <- c("v.3_a.1", "v.3_a.2", "v.3_a.3") varinput.4 <- c("v.4_a.1", "v.4_a.2", "v.4_a.3") names.varinput <- c(varinput.1, varinput.2, varinput.3, varinput.4) ## Provide inference parameters. type.tnorm <- "MIN" type.snorm <- "MAX" type.implication.func <- "ZADEH" type.model <- "FRBCS" ## Give the name of simulation. name <- "Sim-0" ## Provide new data for testing. newdata<- matrix(c(0.45, 0.5, 0.89, 0.44, 0.51, 0.99, 0.1, 0.98, 0.51, 0.56, 0.55, 0.5), nrow = 3, byrow = TRUE) ## the names of variables colnames.var <- c("input1", "input2", "input3", "input4", "output1") ## Construct rules. ## It should be noted that on consequent parts we define categorical values instead of ## linguistic terms. rule <- matrix( c("v.1_a.2", "and", "v.2_a.2", "and", "v.3_a.3", "and", "v.4_a.2", "->", "3", "v.1_a.2", "and", "v.2_a.3", "and", "v.3_a.1", "and", "v.4_a.3", "->", "1", "v.1_a.2", "and", "v.2_a.2", "and", "v.3_a.2", "and", "v.4_a.2", "->", "2"), nrow = 3, byrow = TRUE) ## Generate frbs object. object <- frbs.gen(range.data = range.data.input, num.fvalinput, names.varinput, num.fvaloutput = NULL, varout.mf = NULL, names.varoutput = NULL, rule, varinp.mf, type.model, type.defuz = NULL, type.tnorm, type.snorm, func.tsk = NULL, colnames.var, type.implication.func, name) ## Plot the shape of membership functions. plotMF(object) ## Predicting using new data. res <- predict(object, newdata) #################################################### ## 3. The following example shows how to convert ## the frbs model into frbsPMML #################################################### ## In this example, we are using the last object of FRBS. ## Display frbsPMML in R objPMML <- frbsPMML(object) ## Write into a file with .frbsPMML extention ## Not run: write.frbsPMML(objPMML, fileName="obj_frbsPMML") ## Read the frbsPMML file into an R object of FRBS obj <- read.frbsPMML("obj_frbsPMML.frbsPMML") ## End(Not run) 

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