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Neural Networks

Neural Networks. Language Project. Neural Network. Neural networks have a large appeal to many researchers due to their great closeness to the structure of the brain, a characteristic not shared by more traditional systems .

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Neural Networks

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  1. Neural Networks Language Project

  2. Neural Network • Neural networks have a large appeal to many researchers due to their great closeness to the structure of the brain, a characteristic not shared by more traditional systems. • In an analogy to the brain, an entity made up of interconnected neurons, neural networks are made up of interconnected processing elements called units, which respond in parallel to a set of input signals given to each. The unit is the equivalent of its brain counterpart, the neuron.

  3. A neural network consists of four main parts: • 1. Processing units. • 2. Weighted interconnections between the various processing units which determine how the activation of one unit leads to input for another unit. • 3. An activation rule which acts on the set of input signals at a unit to produce a new output signal, or activation. • 4. Optionally, a learning rule that specifies how to adjust the weights for a given input/output pair.

  4. Motive

  5. Grammar Used • A-> O; A  | D; A | N ; A | Y; A | S ; A | R;A| e • O-> soma I F     --------body of a neuron • E->dendrite E • I-> id • Y->synapse II     --------- a connection • F->function P • P->( P’) • P’->Z,P’| Z • D-> dendrite I F    -------- input to neuron • N -> neuron E   --- a neuron composed of  soma and dendrite • S->sense Z I   ---- information is supplied to this node • R-> result Z id ---- results are supplied to this node • Z->number

  6. Sample Code • // create first neuron (Logistic and Triangle are functions) soma s1 Logistic(10, 2, 5); dendrite d1 Value(1);dendrite d2 Rand(1,2);neuron n1 s1 d1 d2 ;// create second neuronsoma s2 Triangle(3);dendrite d3 Value(1);dendrite d4 Rand(1,2);neuron n2 s2 d3 d4 ;

  7. // create second neuronsoma s3 Triangle(3);dendrite d5 value(4);dendrite d6 Rand(1,2);neuron n3 s3 d5 d6;// connect neuronssynapse n2 d2;synapse n3 d1;// input sense 1 d1;sense 2 d3;sense 3 d4;// out result 1 n1;

  8. Parts of Language • Our Programming language constitutes of three parts in general 1. Framework initialization 2. Topology implementation 3. Processing engine

  9. Features • Facilitates Back Propagation- With this feature we can go back and trace the assign values of the neurons **** back propagation is used for learning • Can Determine two neurons Train two or more neural networks simultaneously-explanation

  10. Hardware Requirements

  11. Software Requirements

  12. Future Enhancements • Concurrency Issues • Simulated currency by processing layers

  13. Questions

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