Multilayer Fuzzy Extreme Learning Machine Applied to Active classification and Transport of objects using an Unmanned Aerial Vehicle

Based on hierarchical Multilayer Extreme Learning Machine (ML-ELM) and Fuzzy Logic theory (FL), in this paper a Multilayer Fuzzy Extreme Learning Machine (ML-FELM) has been developed with an application to active classification and transport of objects using an indoors Unmanned Aerial Vehicle (UAV). The learning approach that follows the proposed ML-FELM is a forward two-step hierarchical methodology. First, by stacking a number of Fuzzy Autoencoders (FAEs), input data is projected into a feature representation space. Each FAE is functionally equivalent to a Mamdani Fuzzy Logic System of type-1 (T1 FLS). Finally, in the second stage, features achieved by stacking a number of FAEs are classified by using a Fuzzy ELM (FELM) based on T1 FLS theory and ELM. To evaluate the effectiveness of the proposed ML-FELM, a number of other existing machine learning approaches were employed for the active classification and transport of four different geometrical objects. To further ensure the efficiency of the ML-ELM, a number of popular benchmark data sets for classification problems are also suggested. Based on our experimental results and compared to other deep learning strategies, the ML-FELM not only represents a fast machine learning approach, but also produces a high model accuracy for image classification.