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Pattern Association. Introduction Pattern association involves associating a new pattern with a stored pattern. Is a simplified model of human memory.

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Presentation on theme: "Pattern Association. Introduction Pattern association involves associating a new pattern with a stored pattern. Is a simplified model of human memory."— Presentation transcript:

1 Pattern Association

2 Introduction Pattern association involves associating a new pattern with a stored pattern. Is a simplified model of human memory. Types of associative memory: Heteroassociative memory Autoassociative memory Hopfield Net Bidirectional 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 : t s 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 pattern Similarity of input patterns

5 Heteroassociative Memory Architecture

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 Algorithm Initialize weights to zero, w ij =0, where i = 1, …, n and j = 1, …, m. For each training case s:t repeat: x i = s i, where i=1,...,n y i = t j, where j = 1,.., m Adjust weights w ij (new) = w ij (old) + x i y j, where i = 1,.., n and j = 1,.., m

8 Exercise Train a heteroassociative neural network to store the following input and output vectors: 1 -1 -1 -11 -1 1 1 -1 -11 -1 -1 -1 -1 1-1 1 -1 -1 1 1-1 1 Test the neural network using all input data and the following input vector: 0 1 0 -1

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.

10 Autoassociative Memory Architecture

11 Exercise Store the pattern 1 1 1 -1 in an autoassociative neural network. Test the neural network on the following input: 1 1 1 -1 -1 1 1 -1 1 -1 1 -1 1 1 -1 -1 1 1 1 1 0 0 1 -1 0 1 0 -1 0 1 1 0

12 The Hopfield Neural Network Is a recurrent associative memory neural network. Application algorithm Exercise: Store the pattern [1 1 1 0] 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: [0 1 1 0]. Note θ i =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 Exercise Store the vectors representing the following patterns using a BAM: [ 1 -1 1] with the output vector [1 -1] [-1 1 -1] with the output vector [-1 1] θi=0, θj=0 for i = 1,..3 and j=1..2


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