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ANNs3

Neuron consists of three basic components - weights,

thresholds, and a single activation function.

In practice, neurons generally do not fire (produce an output) unless their total input goes above a threshold value.

Activation Functions

An activation function f performs a mathematical operation on the signal output. The activation functions are chosen depending upon the type of problem to be solved by the network. The most common activation functions a are called bipolar continuous and bipolar binary functions. The word "bipolar" is used to point out that both positive and negative responses of neurons are produced for this definition of the activation function.

11.NEURAL NETWORK LEARNING RULES

Our focus in this section will be artificial neural network learning rules.

A neuron is considered to be an adaptive element. Its weights are modifiable depending on the input signal it receives, its output value, and the associated teacher response. In some cases the teacher signal is not available and no error information can be used, thus the neuron will modify its weights based only on the input and/or output. This is the case for unsupervised learning. Let us study the learning of the weight vector wi, or its components wy connecting the j7th input with the i th neuron.. In general, the j th input can be an output of another neuron or it can be an external input. Our discussion in this section will cover single-neuron and single-layer network supervised learning and simple cases of unsupervised learning. Under different learning rules, the form of the neuron s activation function may be different. Note

that the threshold parameter may be included in learning as one of the weights. This would require fixing one of the inputs, say x,. We will assume here that x,, if fixed, takes the value of - 1.