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# NEURAL NETWORKS

NEURAL NETWORKS. &amp; Fuzzy logic. Neural network is dubbed as an replica of a human brain on the lines of its working model The functionality of neural network can be equated to the functions of an human brain How does human brain learn

## NEURAL NETWORKS

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### Presentation Transcript

1. NEURALNETWORKS & Fuzzy logic

2. Neural network is dubbed as an replica of a human brain on the lines of its working model The functionality of neural network can be equated to the functions of an human brain How does human brain learn 1. the human brain transforms the given input to output is the general idea (inputs) (outputs)

3. The human brain consists of small elements called Neurons 1.The neurons collects the signal through a host of fine structures called dendrites 2. the neurons then sends out the electrical activity through a thin stand called Axons

4. 3. These axons are then split into thousands of branches 4.At the end of each branch there is a structure called synapse 5.This synapse sends the electrical activity to other neurons which are interconnected

5. When the recipient neuron traces out that the input is large in size in comparison to its original size, its sends the electrical activity down the axon Synapses play an significant role in transferring the data from one neuron to another So, by changing the effectiveness of the synapses learning occurs A neural network also functions in the exact style A neural network is fused up by a series of small elements called neurons

6. We can train a neural network to perform a particular task The fancy point about a neural network is it can be adjusted and trained so that the input leads to the specific target of output Hence neural n/w is also called as artificial neural n/w This is called supervised learning like a learning of a human brain The human brain generated output based on the inputs given

7. But the neural network is a good adjustor of neurons and the desired and target result can be outputted BASIC STRUCTURE OF A NEURAL NETWORK hidden layer Input layer output layer (weights ) synapses

8. BLOCK DIAGRAM OF NEURAL N/W TARGET INPUTS O/P OUTPUTS ADJUST WEIGHTS The o/p is not matched with the target o/p the weights can be adjusted, this particular flash-point as made neural network a remarkable tool NEURAL NETWORK INCLUDING CONNECTIONS CALLED WEIGHTS COMPARE

9. The connection between neurons are called weights the Weight values are adjusted to get the target output. Take a single neuron, in a n/w it has two modes of operation 1.Training mode 2.Firing mode in training mode the neurons will be trained to fire for a particular input patterns In the firing mode the neuron has two tasks To fire if the given input is form the trained list of input patterns/ fire in case of any similarities 2.vice-versa Lets take a best example of a 3-input neuron X1,x2,x3 are three neurons

10. The neuron here is trained in such a style so as to Case1:output 0(don’t fire) if the input is 111(or)101 Case2:Output1 (fire) if the input is 000 (or) 001

11. Consider the third column which is 010, which is before undefined after firing outputs the value “0” HOW?

12. Lets consider fourth column which is before 0/1 after applying the firing rule also holds the constant value,how!!!! HOW? undefined, after applying firing rule it is 0,HOW?

13. Because it hold maximum similarities with both the cases( case1,case2) The firing rule states that it has to remain undefined because of a tie with the same mechanism of neurons getting trained/adjusted/fired, and outputting the target o/p has made neural n/w instrumental in a many spheres Neural network merged with fuzzy logic ha done wonders in the fields of data mining ETC

14. Fuzzy logic tool was introduced in 1965 by lot fi zadeh Fuzzy means something which is blurred/ hazy Fuzzy logic means is a mathematical tool that deals with uncertainty Haziness persist in any realistic process, fuzzy logic task is to decode exactness out of something which is inexact The human brain has the capability to make a clear distinction between an image and an object even if it is blur Linear computing is able to read just pixels as a set of colours Fuzzy logic capability to solve problems that linear computing is not able to do.

15. Fuzzy logic hence embedded in neural networks show more transparency APPLICATIONS of neural networks : speech recognition Pattern recognition image processing data mining robotics data segmentation and compression

16. Fuzzy logic is used to model systems that has ambiguity or opaqueness ,it can be vagueness/lack of information/miscalculation of measurements EXAMPLE Entity x: to this entity a “short” person may be one whose height is below 4.20 Entity y: to this entity a “short” person may be one whose height is beneath or equal to 3.9 Here “short” is the language descriptor , it applies the same meaning to both x and y but it established that they don’t have a unique definition for short Such type of information associated with dilemma are made feasible to the computers with the tool called fuzzy logic

17. The fuzzy logic incorporates a simple “ IF x AND y THEN z” approach rather than modeling a system mathematically Example: Rather than Dealing the temperature control in terms such as 1. “SP=500f” 2.”T<1000f” 3.”210c<TEMP<220c” Fuzzy logic deals in terms like IF(process is too cool) AND(“ getting colder”) THEN(add heat to the process)

18. 2. IF(process is too hot) AND (process is heating rapidly) THEN (cool the process quickly) Because of this potential to deal with complex tasks fuzzy logic has wide range of application having its share in all household appliances 1.Washing machines 2. Electric rice cookers 3.Speech recognition 4.Stock market predictions 5.High speed trains

19. fuzzy logic in washing machines The washing machine first tests how dirty the laundry is Once it knows how dirty the laundry is it can easily calculate how long it can wash it First it always take a base of 10minutes Then if the cloth put in it is 100% dirty then it adds two minutes (10+2=12 minutes) If the cloth put in the washing machine is 50% dirty then it adds 1 minute to the base of 10min(10+1=11min) The laundry can also be greasy at the same time

20. If the laundry is greasy then add 2minutes to the base of 10min(=12min) If the laundry is 50% greasy then add 1 minute to the base of 10min(=11min) 1. 2. 3. FUZZ MACHINE

21. Shirt 1: 100% dirty( 2min) Shirt 2: 100% clean(0 min) Shirt 3:50% greasy(1min) Total time taken by the washing machine working with fuzz logic is =10(base)+2min+0min+1min = 13min ( for three shirts)

22. ADVANTAGES OF NEURAL N/W AND FUZZY LOGIC High accuracy: neural n/w are able to give the exact result of complex systems Noise tolerance: neural n/w are very flexible with respect to incomplete, missing and noisy data Ease of maintenance neural n/w can be updated with fresh data making them useful for dynamic environments When an element (i.e) neuron fails the other neuron undertakes the task

23. Though the advent and discovery of neural network is dated back to 1943 by warren mc culloch, it has been a wonder tool in networks till date Neural networks has been enhanced and able to hammer out solutions for the problems which are complex for conventional computers/human beings Neural network is merited in many ways with only one setback i,e training the neurons to generate a target o/p Hence neural networks and fuzzy logic are supplementary to computers

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