Date Added: Feb 2012
The Cascade Backpropagation Algorithm (CBA) is the basis of a conceptual design for accelerating learning in ANNs. In this paper, input parameters were texture, aroma and flavour, moisture, free fatty acids. Sensory score was taken as output parameter. Bayesian regularization algorithm was used for training the network. Neurons in each hidden layers varied from 1 to 50. The network was trained with 200 epochs with single and multiple hidden layers. Transfer function for hidden layers was tangent sigmoid and pure linear was output function. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient performance measures were used to test the prediction potential of the developed CBA model.