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RFNNHRC for Treadmill Equipment Use

Writer:realleaderfitnessDate:2016-12-9 09:15:57

When hidden neurons are too numerous, computation loads can overwhelm systems, which then become unsuitable for practical on-line applications. However, too few chosen hidden neurons lead to insufficient learning performance to achieve adequate control. To overcome this problem, we developed a novel recurrent fuzzy neural network heart rate controller for treadmill equipment that maintains high accuracy under both of these conditions. In addition, this RFNNHRC employs local modeling and fuzzy input space partition, dynamic fuzzy rule linguistic descriptions, proper learning based structure for training examples, and parsimonious models with reduced Fig. 1 – The RFNNHRC control scheme.parametric complexity . This novel combination of fuzzy reasoning capabilities and artificial recurrent neural network learning processes may accommodate information uncertainties and compensate for treadmill system nonlinearities and uncertainties. The present scheme for heart rate control was designed to control heart rate according to a pre-set heart rate profile during treadmill exercise using adjustable parameters and an on-line learning algorithm that overcomes problems of nonlinearity. Moreover, updating of connections weights in the RFNNHRC allows effective computation of variable treadmill characteristics and provides robust performance against parameter variations.

This entry was posted in Fitness on Dec 9, 2016

TypeInfo: Fitness

Keywords for the information:treadmill equipment  fitness  treadmill characteristics