How To Match Your Stars Prediction For Upcoming Year 2019-13

The output variable to be predicted is the fuel consumption in MPG. The left-most input variable in Figure 1 has the least error or in other words the most relevance with respect to the output. Single layer network use a step function to convert the net input, which is a continuously valued variable, to an output unit that is a binary or bipolar signal. In the following example, exhsrch is used to determine the one most influential input attribute in predicting the output. This means we can push a little further and explore if we can select more than one input attribute to build the ANFIS model. The plot and results from the function clearly indicate that the input attribute ‘Weight’ is the most influential. The results from exhsrch indicate that Weight and Year form the optimal combination of two input attributes. The plot shows the result of selecting three inputs, in which Weight, Year, and Acceler are selected as the best combination of three input variables. However, this will not necessarily be the optimal combination of two inputs that result in the minimal training error. It may not be prudent to use more than two inputs for building the ANFIS model.

Now that the inputs are fixed, we can spend more time on ANFIS training (100 epochs). The plot above shows the error curves for 100 epochs of ANFIS training. The minimal checking error occurs at about epoch 45, which is indicated by a circle. ANFIS model at the minimal checking error during the training process. The plot of the errors provides useful information about the training process. The training and checking errors are getting distinguished, indicating the outset of overfitting. The training and checking errors are comparable, which implies that there is no overfitting. The green curve gives the training errors and the red curve gives the checking errors. The ANFIS prediction can be compared against a linear regression model by comparing their respective RMSE (Root mean square) values against checking data. Therefore we will stick to the two-input ANFIS for further exploration. Online reviews will give you a fair idea, but you cannot base whole of your decision on these online reviews.

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