In this paper, we determine an optimal configuration for characteristics of a multilayer perceptron neural network (MPL) in nonlinear regression for predicting crop yield. Monte Carlo simulation approach has been… Click to show full abstract
In this paper, we determine an optimal configuration for characteristics of a multilayer perceptron neural network (MPL) in nonlinear regression for predicting crop yield. Monte Carlo simulation approach has been used to train several databases generated by varying the internal structure of 3-MLP from simple to complex for five (5) different algorithms most commonly used. Results showed that the optimal configuration is obtained with the Levenberg Marquard algorithm, 75% of the number of input variables as number of hidden nodes, learning rate 40%, minimum sample size 150, tangent hyperbolic and exponential functions in the hidden and output layers respectively. This configuration has been illustrated with real life data.
               
Click one of the above tabs to view related content.