A Firefly Optimized Multi Adaptive Parallel Neuro Fuzzy Inference System
Durgam Revathi · International Journal of Engineering Research and · 2020
In this research the firefly optimized multi adaptive parallel neuro-fuzzy inference (FOMAPNFI) system introduce for improving the performance of the system.This method is inspired from fireflies which produce short flashes as a protective system and to attract mates or prey.The rate and rhythm of the flashes, as well as the time interval between them attract two sexes toward each other.The intensity of light is decreased following an increase in distance from light source.The transmitted light is used as the formulated objective function.Three important properties of FA algorithm are: 1) Firefly is brighter and more attractive when it moves accidentally and all fireflies are unisexual; 2) Attractiveness of firefly is proportional to the brightness and distance from it, and decrease in light intensity is calculated by light absorption coefficient.Brightness of firefly is determined by the objective function value.3) The distance between each firefly is obtained through objective function.Then consider the Multi adaptive parallel neurofuzzy inference system along with additional optimized parameters by using firefly algorithm.This research is used for removing the impulse noise efficiently rather than other methods.Each member of a group of fireflies moved toward a point where their best experience had occurred.By using this approach the best and multiple membership parameters are selected.Hence it is used for which improving the performance of denoising superior.