ANN-Unit 1
Syllabus
- Basics Concepts
- Importance of tolerance of impression and uncertainty
- Biological and artificial neuron
- Single layer perceptron
- Multi-layer perceptron
- Supervised Learning
- Unsupervised Learning
- Back Propagation Network
- Kohenen’s Self Organising Network
- Hopefield Network
Introduction to ANN
- ANN is an efficient information processing system which resembles in characteristics with BNN
- ANN posses large number of highly interconnected processing elements called nodes or units or neurons, which usually operate in parallel and are configured in regular architectures.
- Each neuron is connected with the other by a connection link.
- Each connection link is associated with weights which contain information about he input signal.
- This infomration is used by the neuron net to solve a particular problem.
- ANN's collective behaviour is charactersied by their ability to learn, recall and geralise taining patterns or data similar to that of a human brain.
- They have the vcapapblity to model network of origitnal neruon as found in the brain.
- Thus, the ANN processing elements are called neurons or artifiical neurons.
- It should b noted that each neuron has an internal state of its own.
- This internal state is called the activation or activity level of neuron which is the function of the inputs the neuron revieves.
- The activation signal of a neuron is transmitted to other neurons.
- A neuron can send only on signal at a time which can be transmitte dto several other neurons.
- Simple neuron net architecture , the net input has to be calculated in the following way:
Y_in = x1w1 + x2w2