TUTORIAL- Introduction
- Basic models
- Terminologies
- Supervised Learning Networks
- Perceptron Networks
- Adaptive Linear Neuron
- Back Propagation
- Associate Memory Network
- Bidirectional Associative Memory
- Hopfield Networks
- Unsupervised Learning
- Fixed Weight Competitive Networks
- Kohonen Self-Organizing Feature Maps
- Learning Vector Quantization
- Counter Propagation Networks
Terminologies
Weights
In ANN architecture, every neuron is connected to other neurons by means of a directed communication link and every link is associated with weights. Weight is a parameter which contains information about the input signal. This information is used by the net to solve a problem.
Wij is the weight from processing element ´i´ source node to processing element ´j´ destination node.
Bias (b)
The bias is a constant value included in the network. Its impact is seen in calculating the net input. The bias is included by adding a component x0 =1 to the input vector X.
The bias can also be explained as follows: Consider an equation of straight line, y = mx + c where x is the input, m is the weight, c is the bias and y is the output. Thus, bias plays a major role in determining the output of the network.
Bias can be positive or negative. The positive bias helps in increasing the net input of the network. The negative bias helps in decreasing the net input of the network.
Threshold (Θ)
Threshold is a set value used in the activation function. In ANN, based on the threshold value the activation functions are defined and the output is calculated.
Learning Rate (α)
The learning rate is used to control the amount of weight adjustment at each step of training. The learning rate ranges from 0 to 1. It determines the rate of learning at each time step
Next Topic :Perceptron Networks