Threshold neural network node

Neural networks are essentially non-linear regression models based on a binary threshold unit (McCulloch and Pitts, 1943). The structure of neural networks, called a perception, consists of a set of nodes at different layers where the node of a layer is linked with all the nodes of the next layer (Rosenblatt, 1962). The role of the input layer is to feed input patterns to intermediate layers (also called hidden layers) of units that are followed by an output result layer where the result of computation is read-off. Each one of these units is a neuron that computes a weighted sum of its inputs from other neurons at a previous layer, and outputs a one or a zero according to whether the sum is above or below a... 

The basic feedforward neural network performs a non-linear transformation of the input data in order to approximate the output data. This net is composed of many simple, locally interacting, computational elements (nodes/neurons), where each node works as a simple processor. The schematic diagram of a single neuron is shown in Fig 1. The input to each i-th neuron consists of a A-dimensional vector X and a single bias (threshold) bj. Each of the input signals Xj is weighted by the appropiate weight Wij, where] = 1- N. 

The BPNN is made of three layers, the first layer is made of 22 nodes, the second layer consists of 25 nodes, and the last layer consists of a node, whose value is 0 for normal users, 1 for the intruder. After the BPNN is trained successfully, that is, its weight and threshold values are selected, the procedures running in the network can be monitored and evaluated with the neural network. Experimental result is as shown in Table 1. 

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