Explain in detail Neural Networks?




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Explain in detail Neural Networks?..

Answer / john martin

An artificial neural network is a mathematical or
computational model for information processing based on a
connectionist approach to computation.
There is no precise agreed definition amongst researchers as
to what a neural network is, but most would agree that it
involves a network of relatively simple processing elements,
where the global behaviour is determined by the connections
between the processing elements and element parameters.
The original inspiration for the technique was from
examination of bioelectrical networks in the brain formed by
neurons and their synapses. In a neural network model,
simple nodes (or "neurons", or "units") are connected
together to form a network of nodes — hence the term "neural
network".

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Explain in detail Neural Networks?..

Answer / b.sunil kumar reddy

A neural network can be defined as a model of reasioning
based on the human brain.
The human brain incorporates nearly 10 billion neurons and
60 trillion connections,Synapses,between them.
By using multiple neurons simultaneously,the brain can
perform its functions much faster than the faster computers.

Although a single neuron has a very simple structure,an
army of such elements constitutes a tremendous processing
power.
The network which represents the connections among several
neurons is called a neural network.

Is This Answer Correct ?    16 Yes 8 No

Explain in detail Neural Networks?..

Answer / lipika priyadarshini bhoi

Neural network has the high computational rate than the
other conventional computer.
Neural network can perform tasks that a linear program can not.
When an element of the neural network fails,it can continue
without any problem by their parallel nature.
A neural network learns & does not need to be reprogrammed.

Is This Answer Correct ?    6 Yes 0 No




Explain in detail Neural Networks?..

Answer / sun

The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages:
1. Biological neural networks are made up of real biological neurons that are connected or functionally related in a nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
2. Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex: artificial neural network algorithms attempt to abstract this complexity and focus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, low generalization error), or performance mimicking animal or human error patterns, can then be used as one source of evidence towards supporting the hypothesis that the abstraction really captured something important from the point of view of information processing in the brain. Another incentive for these abstractions is to reduce the amount of computation required to simulate artificial neural networks, so as to allow one to experiment with larger networks and train them on larger data sets.
This article focuses on the relationship between the two concepts; for detailed coverage of the two different concepts refer to the separate articles: biological neural network and artificial neural network

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A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 5 and 20 respectively. The output will be: a) 238 b) 76 c) 119 d) 123

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 A 3-input neuron is trained to output a zero when the input is 110 and a one when the input is 111. After generalization, the output will be zero when and only when the input is: a) 000 or 110 or 011 or 101 b) 010 or 100 or 110 or 101 c) 000 or 010 or 110 or 100 d) 100 or 111 or 101 or 001

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I need a MATLAB source code to recognize different regular geometric shapes such as: squares,rectangles,triangles,circles and ellipses in different sizes using neural network. All of the images containing these shapes should be in binary format with the size of 300*400 pixels. would you please give me a MATLAB code to detect these geometric shapes?

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. Why are linearly separable problems of interest of neural network researchers? a) Because they are the only class of problem that network can solve successfully b) Because they are the only class of problem that Perceptron can solve successfully c) Because they are the only mathematical functions that are continue d) Because they are the only mathematical functions you can draw

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The network that involves backward links from output to the input and hidden layers is called as ____. a) Self organizing maps b) Perceptrons c) Recurrent neural network d) Multi layered perceptron

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