# Fuzzy Neural Networks--A Review with Case Study
## 5 Results  
The study achieved the following results (presented in Table 3):  
* The ANFIS network--As a result of learning for 240 epochs, this network was able to recognize 125 out of 150 samples correctly, giving 83.33% correct solutions for full dataset and 100% when dataset the was limited to two classes: Setosa and Versicolour;
* Fuzzy network with the application of Ordered Fuzzy Numbers (FNN) [74]--For this network, it was necessary to limit the set to two classes: Setosa, Veriscolour. The network had an input layer of four neurons, a deep layer of one neuron, and an output layer of two neurons. After 550 learning epochs it was able to recognize 99.97% of the flowers;
* Scalable deep fuzzy neural network using OFN (SFNN) [75]--The network had an input layer of four neurons, a deep layer of two neurons, and an output layer of one neuron, and after 5000 learning epochs, with learning rate 0.002, the network was able to recognize 83.33% of the flowers for the full dataset and 100% when the dataset was limited to two classes: Setosa and Veriscolour,
* KNeighborsClassifier (KNN) was implemented in TensorFlow library for the following parameters: n_neighbors = 100, weights = 'distance'. The network was able to recognize 93.33% of flowers for full dataset and 100% when the dataset was limited to two classes: Setosa and Veriscolour,
* Decision Tree Classifier (DTC) was implemented in TensorFlow library for the following parameters: criterion = 'entropy', max_depth = 30, min_samples_split = 2. The network was able to recognize 96.67% of flowers correctly for the full dataset and 100% when the dataset was limited to two classes: Setosa and Veriscolour,
* Random Forest Classifier (RFC) was implemented in the TensorFlow library. The network was able to recognize 96.67% of the flowers correctly for the full dataset and 100% when the dataset was limited to two classes: Setosa and Veriscolour. Table 4 presents a comparison of the networks based on the number of neurons and epochs used during training. It is evident that networks utilizing OFNs are smaller and require less computing power during operation. However, their learning process requires more epochs. Table 5 presents the results of the ANFIS network and SFNN for the full Iris dataset recognition. Figures 5 and 6 present the ROC curve for this network. The confusion matrix summarizes the performance of a classification model by showing the number of correct and incorrect predictions for each class. It includes values like true positives (correctly predicted positive samples), true negatives (correctly predicted negative samples), false positives (negative samples incorrectly classified as positive), and false negatives (positive samples incorrectly classified as negative). From this, various performance metrics can be derived. Accuracy measures the overall correctness of the model by calculating the proportion of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms/Network** & **Number of Neurons** & **Number of Epochs** \\ \hline ANFIS & 258 & 240 \\ \hline FNN & 7 & 550 \\ \hline SFNN & 7 & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Neuron and epoch numbers for the used networks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms/Network** & \
\begin{tabular}{c} **True Recognition for** \\ **Limited Iris Dataset [\%]** \\ \end{tabular} &
\begin{tabular}{c} **True Recognition for Full** \\ **Iris Dataset [\%]** \\ \end{tabular} \\ \hline ANFIS & 100 & 83.33 \\ \hline FNN & 99.97 & not possible \\ \hline SFNN & 100 & 95.34 \\ \hline KNN & 100 & 93.33 \\ \hline DTC & 100 & 96.67 \\ \hline RFC & 100 & 96.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results for Iris dataset classification. correctly classified samples out of all predictions. Precision focuses on the quality of positive predictions, indicating the proportion of correctly identified positive samples among all samples predicted as positive.