08-16-2017, 09:16 PM
Classification Of Soil using Neural Network
The objective of this Project is to determine the effectiveness of artificial neural networks for the classification of bare soils.The factors that affect the naming of soil are quite complex. Based on the three main factors (plastic index, liquid limit water capacity, and clay content), and by using a typical neural network back-propagation model, we can classify Soil.A single-layer network has severe restrictions: the class of tasks that can be accomplished is very limited. In this chapter we will focus on feed-forward networks with layers of processing units. Minsky and Papert (Minsky & Papert, 1969) showed in 1969 that a two layer feed-forward network can overcome many restrictions, but did not present a solution to the problem of how to adjust the weights from input to hidden units. An answer to this question was presented by Rumelhart, Hinton and Williams in 1986 (Rumelhart, Hinton, & Williams, 1986), and similar solutions appeared to have been published earlier (Werbos, 1974; Parker, 1985; Cun, 1985). The central idea behind this solution is that the errors for the units of the hidden layer are determined by back-propagating the errors of the units of the output layer. For this reason the method is often called the back-propagation learning rule. Back-propagation can also be considered as a generalisation of the delta rule for non-linear activation functions1 and multilayer networks.