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processing. The whole procedure is demonstrated in Fig. 5. We found that: This model presents an attempt to design an encryption system based on artificial neural networks of the backpropagation type. The proposed ANN has been tested for various numbers of plain text. The simulation results have shown very good results.

Figure 5 processing. The whole procedure is demonstrated in Fig. 5. We found that: This model presents an attempt to design an encryption system based on artificial neural networks of the backpropagation type. The proposed ANN has been tested for various numbers of plain text. The simulation results have shown very good results.

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Figures 1: A general three layer neural network An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a_ specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well. Backpropagation network is one of the most complex neural networks for supervised learning. Regarding topology, the network belongs to a _ multilayer feedforward neural network. See Fig. 1 (Volna 2000), usually a fully connected variant is used, so that each neuron from the n-th layer is connected to all neurons in the (1+J)-th layer, but it is not necessary and in general some connections may be missing — see dashed lines, however, there are no connections between neurons of
Figures 1: A general three layer neural network An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a_ specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well. Backpropagation network is one of the most complex neural networks for supervised learning. Regarding topology, the network belongs to a _ multilayer feedforward neural network. See Fig. 1 (Volna 2000), usually a fully connected variant is used, so that each neuron from the n-th layer is connected to all neurons in the (1+J)-th layer, but it is not necessary and in general some connections may be missing — see dashed lines, however, there are no connections between neurons of
the same layer. A subset of input units has no input connections from other units; their states are fixed by the problem. Another subset of units is designated as output units; their states are considered the result of the computation. Units that are neither input nor output are known as hidden units. A basic computational element is often called a neuron (Fig. 2), node or unit (Fausett 1994). It receives input from some other units, or perhaps from an external source. Each input has an associated weight w, which can be modified so as to model synaptic learning. The unit computes some function f of the weighted sum of its inputs (3):
the same layer. A subset of input units has no input connections from other units; their states are fixed by the problem. Another subset of units is designated as output units; their states are considered the result of the computation. Units that are neither input nor output are known as hidden units. A basic computational element is often called a neuron (Fig. 2), node or unit (Fausett 1994). It receives input from some other units, or perhaps from an external source. Each input has an associated weight w, which can be modified so as to model synaptic learning. The unit computes some function f of the weighted sum of its inputs (3):
Figures 3: A simple example of the encryption system
Figures 3: A simple example of the encryption system
Figures 4: The Error function history SENDING AND RECEIVING MESSAGES
Figures 4: The Error function history SENDING AND RECEIVING MESSAGES
Table 1: Table of experiment constants
Table 1: Table of experiment constants
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