library(frbs) ## Input data data(frbsData) data.train <- frbsData$GasFurnance.dt[1 : 204, ] data.fit <- data.train[, 1 : 2] data.tst <- frbsData$GasFurnance.dt[205 : 292, 1 : 2] real.val <- matrix(frbsData$GasFurnance.dt[205 : 292, 3], ncol = 1) range.data<-matrix(c(-2.716, 2.834, 45.6, 60.5, 45.6, 60.5), nrow=2) ## Set the method and its parameters method.type <- "SBC" control <- list(r.a = 0.5, eps.high = 0.5, eps.low = 0.15, name = "Sim-0") ## Generate fuzzy model object <- frbs.learn(data.train, range.data, method.type, control) ## Fitting step res.fit <- predict(object, data.fit) ## Predicting step res.test <- predict(object, data.tst) ## Error calculation y.pred <- res.test y.real <- real.val bench <- cbind(y.pred, y.real) colnames(bench) <- c("pred. val.", "real. val.") print("Comparison SBC Vs Real Value on Gas Furnace Data Set") print(bench) residuals <- (y.real - y.pred) MSE <- mean(residuals^2) RMSE <- sqrt(mean(residuals^2)) SMAPE <- mean(abs(residuals)/(abs(y.real) + abs(y.pred))/2)*100 err <- c(MSE, RMSE, SMAPE) names(err) <- c("MSE", "RMSE", "SMAPE") print("SBC: Error Measurement: ") print(err) ## Comparing between simulation and real data op <- par(mfrow = c(2, 1)) x1 <- seq(from = 1, to = nrow(res.fit)) result.fit <- cbind(data.train[, 3], res.fit) plot(x1, result.fit[, 1], col="red", main = "Gas Furnance: Fitting phase (the training data(red) Vs Sim. result(blue))", type = "l", ylab = "CO2") lines(x1, result.fit[, 2], col="blue") result.test <- cbind(real.val, res.test) x2 <- seq(from = 1, to = nrow(result.test)) plot(x2, result.test[, 1], col="red", main = "Gas Furnance: Predicting phase (the Real Data(red) Vs Sim. result(blue))", type = "l", ylab = "CO2") lines(x2, result.test[, 2], col="blue", type = "l") par(op)