# Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies
## 5 Network Comparison  
In the frame of network operation scrutiny, its operation was compared with the deep network. The idea of the test was to check what size of the network will be necessary to solve the same problem. As a research problem, the detection of the iris flower was chosen (Iris) [54]. The test set consisted of the description of the iris flowers using four attributes. The database contained 150 samples and has been limited to two flower classes. The structures of the tested networks were as follows: Deep Neural Network 1: input layer, four neurons; deep layer, eight neurons; and output layer, two neurons.  
* Fuzzy Neural Network with OFN 1: input layer, 4 neurons; deep layer, 1 neuron; output layer, 2 neurons,
* Fuzzy Network with OFN 2: input layer, 4 neurons; deep layer, 3 neurons; output layer, 2 neurons,
* Fuzzy Network with OFN 3: input layer, 4 neurons; deep layer, 4 neurons; output layer, 2 neurons.  
Overall, 500 epochs were used when training the regular network, and 550 when training the fuzzy network. Iris flower detection error value is presented in Table 1. The conducted test was to recognize the flower. So, the possible results were 0 or 1. Finally, using the test data, the amount of 0 and 1 was calculated. The percentage of the right results was calculated and provided in the table.  
The iterating network learning process is presented in the pictures below (Figures 10-14).  
Figure 10: Deep neural network—learning process.  
As can be noted from the table, the acquired network accuracy results are promising. Whereas, the network learning process can be considered varied. The initial large changes in the error value are noticeable. The developed algorithm allowed us to acquire satisfying results; however, the developed algorithm still needs a lot of work. When we consider many devices used by people in their life, including a number of small ones with low accuracy, the proposed algorithm can be used to acquire the network learning process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Network** & **\% Error** \\ \hline
1 & Deep Neural Network 1 & 0.04220253 \\
2 & Fuzzy Neural Network with OFN 1 & 0.00036679 \\
3 & Fuzzy Neural Network with OFN 2 & 0.00610502 \\
4 & Fuzzy Neural Network with OFN 3 & 0.00284964 \\
5 & Fuzzy Neural Network with OFN 4 & 0.00612133 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network error.  
Figure 11: Fuzzy network 1—learning process.  
Figure 12: Fuzzy network 2—learning process.  
computational power, there is a necessity to possess some algorithms to control devices and programs' status in a simple way. In such places there is no possibility to install solutions that require high computational power and large amounts of memory. Then, possible problems with the devices may affect the quality of life. The authors propose a fuzzy neural network with ordered fuzzy numbers that does not require high computational power and can be used to detect and predict problems with the devices and programs. The results of the conducted tests using an ordinary network and a proposed fuzzy network prove that the proposed network requires less neurons. Therefore, it requires less computational power. So, it can be implemented in small devices used by people to recognize (predict) problems, and consequently raise the quality of life.