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The Treadmill Machine's Recurrent Fuzzy Neural Network Heart Rate Controller

Writer:realleaderfitnessDate:2016-12-9 09:06:59
Neural networks are classified as feed-forward or recurrent according to structure, and it is well known that feed-forward neural networks can closely approximate any continuous function. However, feed-forward neural networks employ static mapping without the aid of delays, and fail to represent dynamic mapping. Although many previously reported feed-forward neural networks have been used to address delays and dynamic problems, these require large numbers of neurons to express dynamic responses. Furthermore, weight calculations do not update quickly and function approximations are dependent on training data.
To overcome these difficulties, we propose the treadmill machine to use a recurrent fuzzy neural network heart rate controller with adjustable parameters and on-line learning algorithms that have potential for wide application in unknown, nonlinear or uncertain dynamic systems such as the fractal characteristics of the heart rate. Recurrent neural networks are dynamic mapping environments that display good control performance in the presence of unknown, nonlinear, uncertain and time-varying model dynamics. Moreover,recurrent neurons have internal feedback loops and can capture dynamic system responses without requiring delayed external feedback. Hence, recurrent neural networks are better suited for dynamic systems than feed-forward neural networks.
This entry was posted in Fitness on Dec 9, 2016

TypeInfo: Fitness

Keywords for the information:treadmill machine  treadmills  fitness  heart rate