The competitive

neural network has two layers designated for the input and output signals.

The ciphertexts are input in the

neural network, and the output results are compared with the known plaintexts to obtain an error function.

The application of the artificial

neural network for the procedure control is one of the best methods for treating any complex problem by preparing sufficient training data and number of nodes to represent the internal features and relationships that connect input and output variables of automation engineer to construct the controller by himself based on his information and experience in the plant; the training of the artificial

neural network depended on the values of the input variables that affected the number of epochs of the

neural network as a result of the hyperbolic tangent function to reduce the training time by using maximum and minimum normalization method between the input and target values as compared with other normalization methods.

Artificial

Neural Network Results for Porosity Prediction

The figures reveal a good fitting between

neural network output and desired rate for ANN topology using 40 hidden neurons, as shown in Figures 5(a) and 5(b).

In this paper, by using the Lyapunov stability theorems and the norm properties of the interconnection matrices of the neural system, some novel sufficient conditions for the existence, uniqueness and the global robust asymptotic stability of the equilibrium point have been obtained for the class of bidirectional associative memory (BAM)

neural networks with multiple time delays.

A constant vector [x.sup.*] = [([x.sup.*.sub.1], [x.sup.*.sub.2], ..., [x.sup.*.sub.n]).sup.T] is said to be an equilibrium point of

neural network model (5) if and only if

The artificial

neural network market is segmented by type, component, and application.

By using

Neural Network we predicted approximately all the cases correctly.

By measuring the optical intensity around each beam splitter during this process, the researchers showed how to detect, in parallel, how the

neural network performance will change with respect to each beam splitter's setting.

Soljacic says that the goal was to look at

neural networks, a field that has seen a lot of progress and generated excitement in recent years, to see "whether we can use some of those techniques in order to help us in our physics research.

In the first training stage, the inputs and desired outputs are provided to the

neural network (NN).