Affiliations: [a] Department of Electronics and Communication Engineering, GIET University, Gunupur, India | [b] Department of Electronics and Communication Engineering, GIFT, Bhubaneswar, India | [c] Department of Electronics and Communication Engineering, Vedang Institute of Technology, Bhubaneswar, India
Correspondence:
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Corresponding author: Priyadarsan Parida, Department of Electronics and Communication Engineering, GIET University, Gunupur, India. E-mail: [email protected].
Abstract: The equalization of digital channels is widely recognized as a nonlinear classification problem. In such scenarios, utilizing networks that approximate nonlinear mappings can be highly advantageous. There has also been extensive research on equalizers based on Radial Basis Function Neural Networks (RBFNNs). This study introduces a training methodology centred on the Improved Butterfly Optimization Algorithm (IBOA) for channel equalization using RBFNN. This approach aims to optimize the performance of RBFNN equalizers by leveraging the IBOA algorithm for training. Previous literature primarily approached the equalization problem as an optimization challenge. In contrast, this study addresses it as a classification problem. This training approach exhibits substantial enhancements compared to conventional metaheuristic algorithms.
