design a DHNN to store a pattern then use the designed DHNN to recall the corrupted pattern

Sure, here's a brief overview of how you can design a DHNN to store and recall patterns:

1. First, choose the number of neurons in the hidden layer of your DHNN. This will depend on the complexity of the patterns you want to store.

2. Initialize the weights between the input layer and the hidden layer randomly.

3. Present the pattern you want to store to the input layer of the DHNN.

4. Compute the output of the hidden layer using the weights and biases.

5. Use a Hebbian learning rule to update the weights between the input layer and the hidden layer. This will strengthen the connections between the neurons that fire together.

6. Repeat steps 3-5 for each pattern you want to store.

7. To recall a pattern, present a corrupted version of the pattern to the input layer of the DHNN.

8. Compute the output of the hidden layer using the updated weights and biases.

9. Use an inverse Hebbian learning rule to update the weights between the hidden layer and the output layer. This will help the DHNN to reconstruct the original pattern.

10. Compute the output of the output layer using the updated weights and biases. This should be a close approximation of the original pattern.

11. Repeat steps 7-10 for each corrupted pattern you want to recall.

I hope this helps! Let me know if you have any other questions.