Quantum-Inspired Features and Parameter Optimization of Spiking Neural Networks for a Case Study from Atmospheric

Identified cluster of atmospheric discharges, sufficiently near from transmissions line, could be an important alarm to support real time decisions. Lightning are important events that affect the electrical power system operation, which are often responsible for transmission lines outages, and can trigger a sequence of events that lead to system collapse. The Brazilian lightning network detection monitors nearly 18 million events monthly and all this data must be processed and analyzed. This paper uses a hybrid model named the Quantum binary-real evolving Spiking Neural Network (QbrSNN) for clustering problem, where the features and parameters of a spiking neural network (SNN) are optimized using the Quantum-Inspired Evolutionary Algorithm with representation Binary-Real (QIEA-BR). The proposed model is applied to atmospheric discharges data, with a significantly higher clustering accuracy than traditional techniques.