# On Applications of Spiking Neural P Systems
## Applications
### Power Systems Fault Diagnosis
In this model, rule neurons are divided into three categories--i.e, general, AND and OR rule neurons. MFRSNPs have also been used in fault diagnoses of metro traction power supply systems [25]. The energy systems of rail transportation systems are called traction power supply systems and play an important role in safe and reliable operations of trains. It is important to note that the identification of the faults in traction power supply systems is important for uninterrupted supplyof the power. Additionally, the identification of the faults is impacted by the uncertain and incomplete operation information received from the SCADA. The MFRSNPS models are very effective in the diagnosis of the fault section while certain/uncertain as well as complete/incomplete informations is received from the SCADA systems. Since the introduction of the first FRSNPS model in [20], different variants of FRSNPS models have been introduced where the value of the spikes inside the neurons is represented by a triangular fuzzy number (TFRSNPS) [27], intuitionistic fuzzy numbers (IFSNPS) [70], interval-valued fuzzy numbers (IVFSNPS) [71; 72], real numbers (rFRSNPS) [21] and trapezoidal fuzzy numbers (tFRSNPS) [73]. Additionally, a temporal fuzzy reasoning spiking neural P systems with real numbers (rTFRSNPS) was proposed [74] by [PERSON], et al. in 2016. It is also important to note that, in tFRSNPS, both the value of the spike and truth value are represented by a trapezoidal fuzzy number in \([0,1]\); in rFRSNPS, both potential and truth value are real numbers in \([0,1]\). However, in TFSNPS, the potential is a triangular fuzzy number and truth value is a real number in \([0,1]\); in IFSNPS, the potential value is an intuitionistic fuzzy number and the CF is a real number in \([0,1]\), and in IVFSNPS, both potential and CF are interval-valued fuzzy numbers. Moreover, the rule neurons of these models have different structures. The tFRSNPS and rFRSNPS models contain proposition neurons and, in general, AND and OR neurons. However, the TFRSNPS, IFSNPS and IVFSNPS models have proposition neurons and AND and OR rule neurons. IVFRSNPS models are flexible and capable of handling incomplete and uncertain messages. The IFSNPS models are also very effective in the identification of faults in power systems where the messages received from the SCADA systems are incomplete and uncertain. The interval-valued fuzzy reasoning spiking neural P systems (IVFSNPS) [71] introduced in 2019 can also process the incomplete and uncertain messages and can identify the faulty sections efficiently. The efficiency of the reasoning algorithm plays an important role in the efficiency of system and identification of faults quickly and effectively. In tFRSNPS, the inference ability of these models was developed by introducing a matrix-based fuzzy reasoning algorithm which is based on dynamic firing mechanism. The rFRSNPS model proposed in [21; 75], has been used for the fault diagnosis of electric locomotive systems where the relationship between the breakdown signals and faulty sections in the locomotive unit have been represented by the fuzzy production rules. These rules are further simulated by the rules in rFRSNPS. More specifically, in [21; 75] fault diagnosis model for Shaoshan4 (SS4) electric locomotive systems has been investigated. In [76], the rFRSNPS model was used for classification of ten types of faults occurring in the lines of power systems. The proposed method in [76] is a combination of wavelet transform, singular value decomposition and a rFRSNPS model. Moreover, the proposed method is more feasible and effective and in comparison with benchmark methods has superiority in robustness of noise. In 2018, another fault line detection method based on the rFRSNPS model was introduced [77] by [PERSON] et al. More specifically, this model was introduced for fault diagnosis in small current grounding systems. Additionally, steady and transient component features present in the current or voltage signals were considered in this paper.