2 IntroductionPattern association involves associating a new pattern with a stored pattern.Is a “simplified” model of human memory.Types of associative memory:Heteroassociative memoryAutoassociative memoryHopfield NetBidirectional Associative Memory (BAM)
3 Introduction These are usually single-layer networks. The neural network is firstly trained to store a set of patterns in the form s : ts represents the input vector and t the corresponding output vector.The neural network is then tested on a set of data to test its “memory” by using it to identify patterns containing incorrect or missing information.
4 Introduction Associative memory can be feedforward or recurrent. Autoassociative memory cannot hold an infinite number of patterns. Factors that affect this:Complexity of each patternSimilarity of input patterns
6 Heteroassociative Memory The inputs and output vectors s and t are different.The Hebb rule is used as a learning algorithm or calculate the weight matrix by summing the outer products of each input-output pair.The heteroassociative application algorithm is used to test the algorithm.
7 The Hebb AlgorithmInitialize weights to zero, wij =0, where i = 1, …, n and j = 1, …, m.For each training case s:t repeat:xi = si , where i=1,...,nyi = tj, where j = 1, .., mAdjust weights wij(new) = wij(old) + xiyj, where i = 1, .., n and j = 1, .., m
8 ExerciseTrain a heteroassociative neural network to store the following input and output vectors:Test the neural network using all input data and the following input vector:
9 Autoassociative Memory The inputs and output vectors s and t are the same.The Hebb rule is used as a learning algorithm or calculate the weight matrix by summing the outer products of each input-output pair.The autoassociative application algorithm is used to test the algorithm.
11 ExerciseStore the pattern in an autoassociative neural network.Test the neural network on the following input:
12 The Hopfield Neural Network Is a recurrent associative memory neural network.Application algorithmExercise: Store the pattern [ ] using a Hopfield neural network. Test the neural network to see whether it is able to correctly identify an input vector with two mistakes in it: [ ]. Note θi=0, for i=1,..,4
13 Bidirectional Associative Memory (BAM) Consists of two layers, x and y.Signals are sent back and forth between both layers until an equilibrium is reached.An equilibrium is reached if the x and y vectors no longer change.Given an x vector the BAM is able to produce the y vector and vice versa.Application algorithm
14 BAM ExerciseStore the vectors representing the following patterns using a BAM:[ ] with the output vector [1 -1][ ] with the output vector [-1 1]θi=0, θj=0 for i = 1,..3 and j=1..2
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