# On Applications of Spiking Neural P Systems
## Applications
### Power Systems Fault Diagnosis
Information gain degree is an important aspect of feature information fusion and the weight of the importance of features is measured by it. In [77], these features were reduced using the rough set theory. Another fault diagnosis model based on the trapezoidal fuzzy reasoning spiking neural P systems was introduced in [19] by [PERSON] et al. in 2015. This model was named FDSNPS and a matrix-based fuzzy reasoning algorithm was introduced. The strictly mathematical expression of this model helps this model to be intuitively illustrative, and also, since it can effectively handle the incomplete and uncertain messages, this model has good fault tolerant capacity. Moreover, the trapezoidal fuzzy reasoning spiking neural P systems framework is capable of representing the relationship between the PRs and the faults efficiently and hence the model becomes easily understandable. Additionally, the fault diagnosis model introduced in [19] is capable of performing the task of identifying single/multiple faults when incomplete and uncertain messages are received from SCADA systems. Triangular fuzzy reasoning spiking neural P systems (TFRSNPS) were introduced in [27], by the integration of the idea of triangular fuzzy numbers in SNPS. The effectiveness and ability of the fuzzy reasoning method based on this model have been investigatedover the ring network with 220 kV. More specifically, the reasoning method has been used to identify the fault in the bus, line and transformer in the ring network. This process is performed after the PRs and CBs receive incomplete and uncertain messages from SCADA. The reasoning algorithm based on this model is fast and highly accurate, while longitudinal differential protection has been considered. Another new variant of FRSNPS was introduced in 2015 by [PERSON], et al. [24] which is conceptually different from the previous models. In this model. weights are associated with the synapses connecting the neurons and it is known as weighted fuzzy reasoning spiking neural P systems (WFRSNPs). Moreover this model was used for fault diagnosis in traction power supply systems, which generally occur in high-speed railways. The neurons of this system are of four types--i.e., proposition neuron, general, AND and OR rule neurons. The relationship between the PRs and CBs and the faults can be easily represented in the framework of WFNNPS and also a weighted matrix-based reasoning algorithm was introduced in [24]. Additionally, the proposed method has satisfying results for fault diagnosis in normal supply and over zone feeding where the information received from the SCADA system is complete/incomplete. Moreover, with simple reasoning this method can obtain good results. This idea of associating weights with the synapses was further extended in [22] by [PERSON] et al. It is well-known that the machine learning algorithms deal with updating the weights in the neural networks and these algorithms have been used to solve many problems in neural networks. This idea was extended for spiking neural P systems in [22] where the weights associated with the synapses were updated using the learning algorithms in the framework SNPS. Additionally, it was used very effectively in identifying the faults in power systems. Adaptive fuzzy spiking neural P systems (AFSNPs) are one of the variants of WFNNPS. The problem of fault diagnosis of power systems was solved in the framework of AFSNPS in [22] by [PERSON] et al., where the rules were constructed in such a manner that they could simulate weighted fuzzy production rules. Moreover, the adjustment of the weights on the synapses connecting the proposition neurons and rule neurons helped the system to adjust automatically. Additionally, AFSNPS models can perform dynamic fuzzy reasoning. Along with simple reasoning processes, this method is faster compared to other algorithms and has parallel processing capabilities. In AFSNPS, the weights are updated using the Widrow-Hoff learning rules.