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This article mainly introduces "what is the concept of matlab discrete Hopfield neural network". In daily operation, I believe that many people have doubts about the concept of matlab discrete Hopfield neural network. Xiaobian consulted all kinds of data and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts of "what is the concept of matlab discrete Hopfield neural network?" Next, please follow the editor to study!

In 1982, J.Hopfield proposed an interconnection network that can be used as associative memory, which is called Hopfield network model, also known as Hopfield model. Hopfield neural network model is a kind of cyclic neural network, which has feedback connection from output to input. There are two types of Hopfield networks: discrete type and continuous type.

Because the output of the feedback neural network is fed back to its input, the Hopfield network will produce continuous state changes under the input excitation. When there is input, the output of the Hopfield can be extracted, and this output is fed back to the input to produce a new output, and the feedback process continues. If the Hopfield network is a stable network that can converge, the change caused by the feedback and iterative calculation process is getting smaller and smaller, once it reaches the stable equilibrium state, then the Hopfield network will output a stable constant value. For a Hopfield network, the key is to determine its weight coefficient under stable conditions.

% clear environment variables

Clc

Clear

%% data import

Load data1 array_one

Load data2 array_two

%% training sample (target vector)

T = [array _ one;array_two]'

Create network

Net=newhop (T)

Noisy digital lattice of%% digits 1 and 2 (fixed method)

Load data1_noisy noisy_array_one

Load data2_noisy noisy_array_two

%% digit recognition

Noisy_one= {(noisy_array_one)'}

Identify_one=sim (net, {10jue 10}, {}, noisy_one)

Identify_one {10}'

Noisy_two= {(noisy_array_two)'}

Identify_two=sim (net, {10jue 10}, {}, noisy_two)

Identify_two {10}'

%% results show

Array_one=imresize (array_one,20)

Subplot (3, 2, 1)

Imshow (Array_one)

Title ('standard (number 1)')

Array_two=imresize (array_two,20)

Subplot (3, 2, 2)

Imshow (Array_two)

Title ('standard (number 2)')

Subplot (3, 2, 2, 3)

Noisy_array_one=imresize (noisy_array_one,20)

Imshow (Noisy_array_one)

Title ('noise (number 1)')

Subplot (3, 2, 4)

Noisy_array_two=imresize (noisy_array_two,20)

Imshow (Noisy_array_two)

Title ('noise (number 2)')

Subplot (3, 2, 5)

Imshow (imresize (identify_one {10}', 20))

Title ('identify (number 1)')

Subplot (3, 2, 6)

Imshow (imresize (identify_two {10}', 20))

Title ('identify (number 2)')

At this point, the study of "what is the concept of matlab discrete Hopfield neural network" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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