Open AccessOptimized Extreme Learning Machine
Author(s) -
Roshan Kaloni,
Tejas R Nayak,
Mitanshu Sankhe,
Mr. Govind Wakure
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41514
Subject(s) - extreme learning machine , computer science , benchmark (surveying) , feedforward neural network , particle swarm optimization , artificial neural network , artificial intelligence , feed forward , machine learning , algorithm , engineering , geodesy , control engineering , geography
Extreme Learning Machine (ELM) is a learning method for single-hidden layer feedforward neural network (SLFN) training. The ELM strategy speeds up learning by generating input weights and biases for hidden nodes at random rather than modifying network parameters, making it much faster than the standard gradient-based approach. In this project, an ELM optimized by Hybrid Particle Swarm Optimization approach is presented to optimize the input weights and hidden biases for ELM. We will analyze and obtain results for benchmark datasets. The Optimized Extreme Learning Machine algorithm's output is compared to publicly available data. Later we will compare different algorithms and check which one gives better output metrics. Keywords: ELM, SLFN, PSO, Gradient-based approach, Optimization