The Experiment Using the feed forward back propagation
network with, average COF, COF variation and thickness loss as an input and composite materials weight percentage as the output, the network was designed in three layers.
In this work, feed forward back propagation neural network is used to classify the gestures.
The network structure and the training plot of feed forward back propagation neural network is shown in Fig.
The proposed classifier is Multilayer perceptron which is also referred as feed forward back propagation neural network.
ANN was used as decision classifier and feed forward back propagation algorithm was used in training of the neural network.
The two types of neural network concepts such as inductive classifier and feed forward back propagation are used to evaluate the accuracy rate of those neural network concepts.
The modules involved in the development of project are; Image acquisition and Pre-processing; Feature Extraction and Fusion; Inductive Classifier and Error Rate; Feed Forward Back Propagation and Accuracy Rate; Performance Comparison (Inductive Classifier and Feed Forward Back Propagation).
Several ANN topologies have been developed for different applications, the most popular being the Feed Forward Back Propagation
Network, which is shown in Figure 1.
A data consists of 50 experimental data points were used to construct fully developed feed forward back propagation
The feed forward back propagation
model is very popular model in neural network, it does not have feed back connection, but the errors are back propagated during training.
The load forecasting for the next day using AR and ANN models is performed separately, and the results of the AR analysis is used for the input of different ANN models, which are Feed Forward Back Propagation and Cascade Forward Back Propagation Models.
The results of the AR analysis are used for the input of different ANN models which are Feed Forward Back Propagation and Cascade Forward Back Propagation Models.