Perceptrons neural network

1. Perceptrons
The perceptron is the simplest form of a neural network used for
the classification of patterns said to be linearly separable . basically , it
consists of a single neuron with adjustable synaptic weights and bais.
The summing node of the neuronal model computes a linear
combination of the inputs applied to its synapses, and also incorporates an
externally applied bais. The resulting sum, that is applied to a hard
limiter.accordingly, the neuron produced an output equal to +1 if the hard
limiter input is positive, and -1 if it is negative.
In the signal flow graph model of fig. 1 , the synaptic weights of
the perceptron are denoted by w1,w2,….,wm . correspondingly, the
inputs applied to the perceptron are denoted by x1,x2,…,xm.
2. MULTI-LAYER PERCEPTRON
Multi-layer perceptrons represent a generalization of the single-layer perceptron as described in the previous section. The capability of multi-layer perceptron stems from the non-linearities used within the nodes. The Figure 2 shows a typical multi-layer perceptron neural network structure.
consists of the following layers:
Hidden Layer: A layer of neurons that receives information from the
input layer and processes them in a hidden way. It has no direct connections to the outside world (inputs or outputs). All connections from the hidden layer are to other layers within the system. Output Layer: A layer of neurons that receives processed information and sends output signals out of the system.
Bias: Acts on a neuron like an offset. The function of the bias is to provide a threshold for the activation of neurons. The bias input is connected to each of the hidden and output neurons in a network.
How many units to have in each layer? • The number of output units is often determined by the number of output classes. • The number of inputs is determined by the number of input dimensions • The number of hidden units is a design issue. The problems are: – too few, the network will not model complex decision boundaries; – too many, the network will have poor generalisation.