Auto MPG Prediction using Tree Architectures of Fuzzy Neural Networks

For decades, computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are applied to Auto MPG prediction problem. The dataset concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, Auto MPG dataset obtained from the UCI Machine Learning Repository Database is considered.