Artificial Intellignce Essay

493 WordsMay 22, 20152 Pages
BACKPROPAGATION Backpropagation is a kind of neural network. A Neural Network (or artificial neural network) is a collection of interconnected processing elements or nodes. The nodes are termed simulated neurons as they attempt to imitate the functions of biological neurons. The nodes are connected together via links. HOW BACKPROPAGATION WORKS? Initially, a weight is assigned at random to each link in order to determine the strength of one node’s influence on the other. When the sum of input values reaches a threshold value, the node will produce the output 1 or 0 otherwise. By adjusting the weights the desired output can be obtained. This training process makes the network learn. The network, in other words, acquires knowledge in much the same way human brains acquire namely learning from experience. Backpropagation is one of the powerful artificial neural network technique which is used acquire knowledge automatically. Backpropagation method is the basis for training a supervised neural network. The output is a real value which lies between 0 and 1 based on the sigmoid function. The formula for the output is, Output = 1 / (1+e-sum) As the sum increases, the output approaches 1. As the sum decreases, the output approaches 0. A Multilayer Network A multilayer network is a kind of neural network which consists of one or more layers of nodes between the input and the output nodes. The input nodes pass values to the hidden layer, which in turn passes to the output layer. A network with a layer of input units, a layer of hidden units and a layer of output units is a two-layer network. A network with two layers of hidden units is a three-layer network, and so on. The multilayer network is the basis for backpropagation network. As the name implies, there is a backward pass of error to each internal node within the network, which is then used to calculate weight

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