# On Applications of Spiking Neural P Systems
## Applications
### Power Systems Fault Diagnosis
This model was constructed by combining the idea of attribute reduction ability ( concept from rough set) and apoptosis mechanism (from biological neurons) in the framework of membrane computing models--i.e., spiking neural P systems. One of important features of apoptosis algorithms is that these algorithms can devise the conditional neurons in such a manner that they can remove the unnecessary information received from SCADA systems. This further helps the model to perform better while uncertain and incomplete messages are received. The fault diagnosis method in the framework of srSNPS can be divided into four sections--i.e., (1) transmission network partition; (2) construction of SNPS model; (3) the correction of the pulse value; (4) computing along with evaluation of protective device behaviour. The topological adaptive ability is improved by the transmission network partition. Moreover, this method has good ability to interpret the diagnosis result. However, during this process, it maintains high fault tolerance along with fast diagnosis speed, even though the messages received from the SCADA system are uncertain and incomplete. One of the major advantages of this model is that historical statistics and expertise are not required. Other new variant of the SNPS model, which have been used for the task of fault diagnosis, are called electrical synaptic transmission-based spiking neural P systems(ESTSNPs). This model was introduced in [30] by adding some new synapses and neurons, removing the delay and adding bidirectional characteristics of electric synaptic transmission with the original definition of SNPS. Additionally, the rules of this model are on the electric synapse. The fault diagnosis method based on ESTSNPs is very effective and are capable of identifying single/multiple faults, misinformation faults in distribution networks with DGs (distributive generators) with high accuracy quickly. It also has high traceability. All the above models mentioned above are efficient. However the implementations of these models are manual which are also very time consuming as well as inefficient when dealing with large scale networks. In order to solve this problem, an automatic implementation method [28] was introduced in 2019. The method introduced in [28] is called MCFD (membrane computing fault diagnosis) and it can solve the problem of fault diagnosis automatically. Comparisons of the SNPS models solving the fault diagnosis problem are presented in Figures 2-4. **Remark 1**. : _(1) Different variants of SNPS have been used to solve the problem of fault diagnosis of power systems and these models can successfully solve these problems even after receiving incomplete and uncertain messages from SCADA systems. Additionally, these models are capable of finding single/multiple faults. However, these models are not effective when dealing with large scale power systems. It can be a future direction of research whether the above discussed models can be further extended using the properties of fuzzy logic to efficiently solve fault diagnosis problems for large scale networks._  
_(2) The basic system of the automatic implementation method introduced in [28] is an FRSNPS model. It can be a future direction of research to construct similar methods for other variants of FRSNPS and it will also be interesting to study the comparison of the time and space complexity of these methods with the method introduced in [28]._  
_(3) Until now the models used in the fault diagnosis method have static network structures. It can be further investigated whether it is possible to construct fault diagnosis methods using the SNPS models with dynamic network structure, such as SNPS with structural plasticity [14] and SNPS with neuron budding rules [55] or SNPS with neuron division and budding rule [56; 57], etc., which can increase the size of the synapse graph. Furthermore, we can compare the performance of these models with the existing models with static network._