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## cellular neural networksPosted by: electronics seminars Created at: Monday 28th of December 2009 05:07:40 AM Last Edited Or Replied at :Monday 02nd of May 2011 03:07:21 AM | cellular neural networks ppt ,
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onding r.t.d. C.N.N. UNIVERSAL MACHINE The first spatio temporalanalogic array computer is CNN universal machine. The two different operations are continous-time, continues valued spatio-temporal nonlinear array dynamics (2-d$3-darrays) local & global logic hence analog &logic operations are mixed and embedded in array computer. Therefore we call this type of computing: analogic. The CNN-UM architecture (1) It has a minimum number of component types (2) Provides stored programmability spatio temporal array computing (3) Is universa.................. [:=> Show Contents <=:] | |||

## artificial neural network seminar reportPosted by: electronics seminars Created at: Tuesday 05th of January 2010 05:09:09 AM Last Edited Or Replied at :Monday 06th of February 2012 12:22:33 AM | artificial neural networks applications,
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ne and on line weight adaptation Parallel processing architecture allows fast processing for large-scale dynamic system. Neural network can handle large number of inputs and can have many outputs. Neural network architecture have learning algorithm associated with them. The most popular network architecture used for control purpose is multi layered neural network with error propagation algorithm. 3. LEARNING TECHNIQUES Learning rules are algorithm for slowly alerting the connections weighs to achieve a desirable goal such a minimization of an error function. The generalized.................. [:=> Show Contents <=:] | |||

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ar combination F= â€šÂ¬ CiAi (u) Ai(v-j)=1, if i=j VERTEX - CODE GENERATION The first step is to identify the simplex that contains the given input vector u.Notice here that there are n! different simplices in (3), and every simplex is identified by the coordinates of its n+ 1 vertex. This mechanism is based on the result, U= ? B Vij This mechanism can be realized using a mixed signal circuit architecture illustrated where an array of comparators compare components input to ramp signal. In this scheme vertex codes can be read at.................. [:=> Show Contents <=:] | |||

## ARTIFICIAL NEURAL NETWORK BASED POWER SYSTEM RESTORATOINPosted by: computer science crazy Created at: Monday 28th of December 2009 01:47:21 AM Last Edited Or Replied at :Monday 06th of February 2012 12:22:47 AM | RESTORATOIN,
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ing problems. The building block of ANN is Artificial Neuron, which has got structural & functional
similarities with biological neurons. ANN is also an efficient alternative for problem solutions
where it is possible to obtain data describing the problem behavior, but a mathematical description
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when the power system is recovering from a wide area disturbance. CONTENTS 1. I.................. [:=> Show Contents <=:] | |||

## neural network application in power electronicsPosted by: shashimandya Created at: Wednesday 09th of December 2009 08:29:39 AM Last Edited Or Replied at :Friday 20th of January 2012 10:27:22 PM | neural network applications,
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## Neural NetworksPosted by: computer science crazy Created at: Thursday 03rd of September 2009 05:56:13 AM Last Edited Or Replied at :Thursday 03rd of September 2009 05:56:13 AM | neural networks graph ,
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timating power loss in conventional algorithm. ANN are designed to two groups: 1) The first group is to estimate the proper load data of each zone . 2)The second is to determine the appropriate system topology from input load level . In addition, several programs with the training set builder are developed for the design the training & accuracy test of A.N.N.This paper will present the strategy of feeder reconfiguration to reduce power loss, by using A.N.N. The approach developed here is basically different from methods reviewed above on flow solution during search process are not .................. [:=> Show Contents <=:] | |||

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This section leads us to a brief conclusion and we end the paper with the references. http://www.seminarprojects.net/downloads/?file=/computer%20science%20and%20electronics/ARTIFICIAL-NE URAL-NETWORKS.pdf&name=ARTIFICIAL-NEURAL-NETWORKS.pdf.................. [:=> Show Contents <=:] | |||

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f dogs and exhibit some structural capability for generalization. The most significant aspects of Neural Networks are that they allow the computer to learn and they have the potential for parallelism. This means that they allow the computer to solve multiple problems at a time. Neural Networks can perform any variety of tasks just as any regular computer. They are of greatest use in computing problems where the input does not follow clean strict rules but instead has an overall pattern. Neural Networks have applications in diverse areas like interpretation, prediction, diagnosis, plannin.................. [:=> Show Contents <=:] |

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