Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, ekend, holiday and ather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rule base and then only current information like date related information and ather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feed forward neural network in terms of prediction accuracy.
['Syed Moshfeq Salaken', 'Mohammad Anwar Hosen', 'Abbas Khosravi', 'Saeid Nahavandi']
I am writing a research paper on a idea Forecasting Bike Sharing Demand Using Fuzzy Inference Mechanism
[{"from": "human", "value": "I am writing a research paper on a idea Forecasting Bike Sharing Demand Using Fuzzy Inference Mechanism"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, ekend, holiday and ather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rule base and then only current information like date related information and ather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feed forward neural network in terms of prediction accuracy."}]