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Join Date: Apr 2012
Posts: 22

Neural Networks

11/18/2012 5:56 AM

I have 15inputs and want to come up with two outputs which i will scale down to bits for control of electric motor speed through variable speed drives.how can i do this using Neural networks considering the inputs are input barley specifications,from two batchs of malt.Ultimately i will be using the two inputs for blending purposes ie each batch has its own 15parameters.

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Guru

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Location: India
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#1

Re: neural networks

11/18/2012 11:19 AM

One of the LINKS may have your clue.

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Guru
United Kingdom - Member - Indeterminate Engineering Fields - Control Engineering - New Member

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#2

Re: Neural Networks

11/19/2012 3:54 AM

Obtain the P&ID for the plant, showing all valves and instrumentation. Write, and get approval for once written, a Control Philosophy document. Once this is approved, write a Functional Design Specification for the equipment. Once this is approved, the control coding and equipment selection will follow.

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Member

Join Date: Sep 2012
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#3

Re: Neural Networks

11/20/2012 1:06 AM

Hello Friends,

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.

Best Regards
Steven Arnold

Hp Proliant Dl360 G7 Server

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