assosiative memory network - Associative Memory Networks Dr K Ganesan Director TIFAC-CORE in Automotive Infotronics VIT University Vellore 632 014

assosiative memory network - Associative Memory Networks Dr...

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Associative Memory Networks Dr. K. Ganesan Director – TIFAC-CORE in Automotive Infotronics VIT University, Vellore – 632 014 [email protected]
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Introduction
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CAM The CAM can also be viewed as associating data to address, ie, for every data in the memory there is a corresponding unique address. It can be viewed as a data correlator. Here input data is correlated with that of the stored data in the CAM. It should be noted that the stored pattern must be unique, ie different patterns in each location. Associative memory makes a parallel search within a stored data file. The idea is to retrieve any one or all stored items which match the given search argument.
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Types of associative memories There are two types of associative memories. They are autoassociative memory and heteroassociative memory. Both these nets are single-layer nets in which the weights are determined in a manner that the net stores a set of pattern associations. Each of these association is an input-output vector pair, say, s:t. If each of the output vectors is same as the input vectors with which it is associated, then the net is said to be auto associative memory net. If the output vectors are different from the input vectors then the net is said to be heteroassociative memory net.
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Training algorithms for Pattern Association There are 2 algorithms developed for training of pattern association nets. They are : Hebb rule and Outer products rule. Hebb Rule The training algorithm is given below: Step 0: Set all the initial weights to zero, ie, wij = 0 (i =1 to n, j = 1 to M) Step 1: For each training target input output vector pairs s:t, perform Steps 2-4.
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  • Winter '16
  • ganeshan
  • Neural Networks, associative memory, Hopfield net

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