17-04-2013, 03:52 PM
A PAPER PRESENTATION OnTHE ARTIFICIAL INTELLIGENCE & NEURAL NETWORKS
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ABSRRACT
The study of the human brain is thousands of years old . The exact workings of the human brain are still a mystery. Yet, some aspects of this amazing processor are known. In particular, the most basic element of the human brain is a specific type of cell called neuron which is able to remember , think, and apply previous experiences to our every action.
Simulating human brain which and emotion is still the realm of science fiction . With the advent of modern electronics, it was only natural to try to harness this thinking process. The artificial neural networks try to replicate only the most basic elements of this complicated, versatile, and powerful organ of humans.
INTRODUCTION
An artificial neural network is, in essence, an attempt to simulate the brain. Neural network theory revolves around the idea that certain key properties of biological neurons can be extracted and applied to simulations, thus creating a simulated (and very much simplified) brain. The first important thing to understand then, is that the components of an artificial neural network are an attempt to recreate the computing potential of the brain. The second important thing to understand, however, is that no one has ever claimed to simulate anything as complex as an actual brain. Whereas the human brain is estimated to have something on the order of ten to a hundred billion neurons, a typical artificial neural network (ANN) is not likely to have more than 1,000 artificial neurons.
Purpose of Studying:
Neural networks appear to be able to solve "monster" problems of AI that traditional systems have found difficulty with. These include , but are not limited to , speech recognition and synthesis , vision , and pattern recognition.
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.
• Recurrent network
Contrary to feedforward networks , recurrent networks ( RNs ) are models with bi -directional data flow . While a feedforward network propagates data linearly from input to output , RNs also propagate data from later processing stages to earlier stages .
• Simple recurrent network
A simple recurrent network (RN) is a variation on the multi - layer perceptron, sometimes called an " Elman network " due to its invention by Jeff Elman . A three - layer network is used , with the addition of a set of " context units " in the input layer. There are connections from the middle (hidden) layer to these context units fixed with a weight of one. At each time step , the input is propagated in a standard feed - forward fashion , and then a learning rule (usually back-propagation) is applied.
Cognitive Science
The study of Neural Networks was a large break through for Cognitive Science. Because understanding the brain is a key point in understanding Human Cognition, the use of a neural network to model or simulate the brain is extremely beneficial. However because the human brain has so many neurons it is very difficult to model the entire brain with the current technology available . For this reason many researchers study the brain in sections and model Neural Networks after the section of specific interest.
Conclusion
In summary, artificial neural networks are one of the promises for the future in computing. They offer an ability to perform tasks outside the scope of traditional processors . They can recognize patterns within vast data sets and then generalize those patterns into recommended courses of action .Neural networks learn , they are not programmed.
Now neural networks are finding themselves in applications where humans are also unable to always be right . Neural networks can now pick stocks , cull marketing prospects, approve loans, deny credit cards, tweak control systems, grade coins, and inspect work.