Mechanism for automated neural network based transport system with learning

The wide applicability of neural networks to solve various real life problems is well understood. This paper proposes a mechanism for automated neural network based transport system with learning (MANTLE), an automated transport system with considerable advantages over previous attempts. The system uses a multilayer feed-forward neural network with back propagation learning. In addition, the design of MANTLE involves the convergence of a plethora of technologies like Global Positioning System (GPS), a geographic information system (GIS), and laser ranging. MANTLE can guide a mobile agent through a hostile and unfamiliar domain after being trained by a human user with domain expertise. One of the many areas in which MANTLE scores against the competition is that the system is completely domain independent and incurs substantially less processor overhead. MANTLE thus provides more functionality, even though it requires a lot less input as compared to other attempts in this field. This reduction in the size of the input vector translates into more efficient and faster processing. Another of MANTLE's hallmark features is its ability to negotiate turns and implement lane-changing maneuvers with a view to overtaking obstacles. It does this by employing a novel technique, selective net masking. A simulation of MANTLE's neural network was performed on a variety of network topologies, and the best network selected.