Artificial Neural Networks
This Electronics Seminar Topic deals with the followin:
Artificial Neural Networks
Most people when asked if they think computers could ever become sentient quickly respond no and refer to the fact that computers are unable to learn. However, Neural Networks seems to do just that.
Neural Networks encompass a diverse set of computational models, which share a set of simple underlying characteristics. Inspired by the computational style of biological systems, a Neural Network can be viewed as an assembly of simple, interconnected processing units (neurons) acting in parallel, which communicate to each other using unidirectional connections.
Neural networks are distinguished from other computer and mathematical techniques by their design motivation. They are processing devices, that can be algorithms or actual hardware that are modeled after the functioning of human brain. Most Neural Networks have some sort of “training” rule whereby the weights of connections are adjusted on the basis of presented patterns. In other words, Neural Networks “learn” from examples, just like children learn to recognize dogs from examples of dogs and exhibit some structural capability for generalization.
The most significant aspects of Neural Networks are that they allow the computer to learn and they have the potential for parallelism. This means that they allow the computer to solve multiple problems at a time.
Neural Networks can perform any variety of tasks just as any regular computer. They are of greatest use in computing problems where the input does not follow clean strict rules but instead has an overall pattern. Neural Networks have applications in diverse areas like interpretation, prediction, diagnosis, planning, monitoring, debugging, repair, instruction, control, categorization and pattern recognition. Thus Neural Networks is an exponentially growing area of real- time applications of the new era.
Artificial Neural network is a system loosely modeled on the human brain. The field goes by many names, such as connectionism; parallel distributed processing, euro computing, natural intelligent systems, machine learning algorithms and artificial neural networks. It is an attempt to simulate within specialized hardware or sophisticated software, the multiple layers of simple processing elements called neurons. Each neuron is linked to certain of its neighbors with varying coefficients of connectivity that represent the strengths of these connections. Learning is accomplished by adjusting these strengths to cause the overall network to output appropriate results.
THE ANALOGY TO BRAIN
The most basic components of neural networks are modeled after the structure of the brain. Some neural network structures are not closely to that of the brain and some does not have a biological counterpart in the brain. However, neural networks have a strong similarity to the biological brain and therefore a great deal of the terminology is borrowed from neuroscience.