Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The manuscript presents RFNNHRC replacing the PI.RFNN achieves minimal HR deviation & resulted in smaller fluctuation than PIRFNN performs smoother in treadmill speed and incline control by our test.RFNN controls HR with adjusting parameter and online learning algorithm.Experiments in the manuscript proves more stable HRC by RFNN against PI. Background and objectiveIn this study,  developed a computer controlled treadmill system using a recurrent fuzzy neural network heart rate controller (RFNNHRC). Treadmill speeds and inclines re controlled by corresponding control servo motors. The RFNNHRC was used to generate the control signals to automatically control treadmill speed and incline to minimize the user heart rate deviations from a preset profile. MethodsThe RFNNHRC combines a fuzzy reasoning capability to accommodate uncertain information and an artificial recurrent neural network learning process that corrects for treadmill system nonlinearities and uncertainties. Treadmill speeds and inclines are controlled by the RFNNHRC to achieve minimal heart rate deviation from a pre-set profile using adjustable parameters and an on-line learning algorithm that provides robust performance against parameter variations. The on-line learning algorithm of RFNNHRC was developed and implemented using a dsPIC 30F4011 DSP. ResultsApplication of the proposed control scheme to heart rate responses of runners resulted in smaller fluctuations than those produced by using proportional integra control, and treadmill speeds and inclines re smoother. The present experiments demonstrate improved heart rate tracking performance with the proposed control scheme. ConclusionsThe RFNNHRC scheme with adjustable parameters and an on-line learning algorithm was applied to a computer controlled treadmill system with heart rate control during treadmill exercise. Novel RFNNHRC structure and controller stability analyses re introduced. The RFNNHRC re tuned using a Lyapunov function to ensure system stability. The superior heart rate control with the proposed RFNNHRC scheme was demonstrated with various pre-set heart rates.
['Chun-Hao Lu', 'Wei-Cheng Wang', 'Cheng-Chi Tai', 'Tien-Chi Chen']
I am writing a research paper on a idea Design of a heart rate controller for treadmill exercise using a recurrent fuzzy neural network
[{"from": "human", "value": "I am writing a research paper on a idea Design of a heart rate controller for treadmill exercise using a recurrent fuzzy neural network"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The manuscript presents RFNNHRC replacing the PI.RFNN achieves minimal HR deviation & resulted in smaller fluctuation than PIRFNN performs smoother in treadmill speed and incline control by our test.RFNN controls HR with adjusting parameter and online learning algorithm.Experiments in the manuscript proves more stable HRC by RFNN against PI. Background and objectiveIn this study,  developed a computer controlled treadmill system using a recurrent fuzzy neural network heart rate controller (RFNNHRC). Treadmill speeds and inclines re controlled by corresponding control servo motors. The RFNNHRC was used to generate the control signals to automatically control treadmill speed and incline to minimize the user heart rate deviations from a preset profile. MethodsThe RFNNHRC combines a fuzzy reasoning capability to accommodate uncertain information and an artificial recurrent neural network learning process that corrects for treadmill system nonlinearities and uncertainties. Treadmill speeds and inclines are controlled by the RFNNHRC to achieve minimal heart rate deviation from a pre-set profile using adjustable parameters and an on-line learning algorithm that provides robust performance against parameter variations. The on-line learning algorithm of RFNNHRC was developed and implemented using a dsPIC 30F4011 DSP. ResultsApplication of the proposed control scheme to heart rate responses of runners resulted in smaller fluctuations than those produced by using proportional integra control, and treadmill speeds and inclines re smoother. The present experiments demonstrate improved heart rate tracking performance with the proposed control scheme. ConclusionsThe RFNNHRC scheme with adjustable parameters and an on-line learning algorithm was applied to a computer controlled treadmill system with heart rate control during treadmill exercise. Novel RFNNHRC structure and controller stability analyses re introduced. The RFNNHRC re tuned using a Lyapunov function to ensure system stability. The superior heart rate control with the proposed RFNNHRC scheme was demonstrated with various pre-set heart rates."}]