MODELLING & SIMULATION OF HUMAN POWERED FLYWHEEL MOTOR FOR FIELD DATA IN THE COURSE OF ARTIFICIAL NEURAL NETWORK – A STEP FORWARD IN THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE

A. R. Lende, J. P. Modak

Abstract: As per geographical survey of India about 65% of human population is living in rural areas where urban resources like electricity, employment accessibility, etc are very deprived. The country is still combating with fundamental needs of every individual. The country with immense population living in villages ought to have research in the areas which focuses and utilizes the available human power. Some Authors of this paper had already developed a pedal operated human powered flywheel motor (HPFM) as an energy source for process units. The various process units tried so far are mostly rural based such as brick making machine (both rectangular and keyed cross sectioned), Low head water lifting, Wood turning, Wood strips cutting, electricity generation etc. This machine system comprises three sub systems namely (i) HPFM (ii) Torsionally Flexible Clutch (TFC) (iii) A Process Unit. Because of utilization of human power as a source of energy, the process units have to face energy fluctuation during its supply. To evaporate this rise and fall effect of the energy, the concept of use of HPFM was introduced. During its operation it had been observed that the productivity has great affection toward the rider and producing enormous effect on quality and quantity of the product. This document takes a step ahead towards the development of a controller which will reduce system differences in the productivity. This paper contributes in development of optimal model through artificial neural network which enables to predict experimental results accurately for seen and unseen data. The paper evaluates ANN modeling technique on HPFM by alteration of various training parameters and selection of most excellent value of that parameter. The mathematical model of which then could be utilized in design of a physical controller.

Keywords: Artificial Neural Network, HPFM, MATLAB

DOI: https://doi.org/10.15623/ijret.2013.0212047