NEURAL NETWORK AND IT'S INDUSTRIAL USE CASE
What we are going to learn in this block:
- Introduction to NEURAL NETWORK (NN).
- Why Neural Network
- What is Neural Network
- ADVANTAGES and Application OF NEURAL NETWORKS
- Industrial Use cases of Neural Network
Introduction to NEURAL NETWORK (NN):-
Over the past few years, technology has become very dynamic. It is fuelling itself at an ever increasing rate. Computers are a prime component of this whole revolution. Computers that can help fight diseases by designing new drugs, computers that can design better computers, computers that simulate reality and what not! This is a very exciting time for technology as the traditional boundaries are now becoming blurred.
Why Neural Network
The rapid pace of change and a climate of competitiveness, in which the profoundly and speedily informed gain the advantage and demand a more incisive consideration and induction of emerging IT than has hitherto been the case. There are a range of Al technologies available now-each with its own strengths and weaknesses. These are:
- Expert systems
- Fuzzy logic
- Case-based reasoning
- Neural networks
- Genetic algorithms
What is Neural Network
- Neural networks can be taught to perform complex tasks and do not require programming as conventional computers. They are massively parallel, extremely fast and intrinsically fault-tolerant.
- They learn from experience, generalise from examples, and are able to extract essential characteristics from noisy data. They require significantly less development time and can respond to situations unspecified or not previously envisaged.
- They are ideally suited to real-world applications and can provide solutions to a hos' of currently impossible or commercially impractical problems.
- In simple terms, a neural network is made up of a number of processing elements called neurons, whose interconnections are called synapses.
- Each neuron accepts inputs from eitl~er the external world or from the outputs of other neurons. Output signals from all neurons eventually propagate their effect across the entire network to the final layer where the results can be output to the real world.
- The synapses have a processing value or weight, which is learnt during training of the network. The functionality and power of the network primarily depends on the number of neurons in the network, the interconnectivity patterns or topology, and the value of the weights assigned to each synapse.
ADVANTAGES and Application OF NEURAL NETWORKS
Artificial neural networks have become an accepted information analysis technology in a variety of disciplines. This has resulted in a variety of commercial applications (in both products and services) of neural network technology (The applications that neural networks have been put to and the potential possibilities that exist in a variety of civil and military sectors are tremendous.)
Given below are domains of commercial applications of neural network technology.
- Business
- Marketing
- Document & Form Processing
- Machine printed character recognition
- Graphics recognition
- Hand printed character recognition
- Finance Industry
- Market trading
- Weather forecasting
- Energy Industry
- Electrical load forecasting
- Hydroelectric dam operation
- Natural gas
- Manufacturing
Industrial Use cases of Neural Network
Lot of opportunities exist in the country for Al technologies, especially neural computing applications. Though most of the work is being done around robotics and expert systems, there are also people and organisations capable of developing neural system products. The potential sectors of application range from manufacturing, banking and finance, defence, telecommunications, pharmaceuticals to holiday industry. Substantial amount of work is being done at the Centre for Artificial Intelligence and Robotics (CAIR, Bangalore) and the Institute for Robotics and Intelligent Systems (IRIS, Bangalore).
They have developed a neural network for optical character recognition. The project is complete and awaits commercialisation. IRIS is working on functional electrical simulation using neural networks to simulate the muscles of a handicapped person and allow him to walk. Scientists at the Indian Statistical lnstitute (Machine Intelligence Unit), Calcutta, have figured out computer simulated models, more advanced than human brain, for creating artificial entities more intelligent than present day systems in performing cognitive tasks. This project will have far reaching implications on medical research and robotics.