# Nt1310 Unit 7 Lab Report

Good Essays
493 Words
Grammar
Plagiarism
Writing
Score
Nt1310 Unit 7 Lab Report
The steps 1 to 5 denotes the forward propagation
1. First apply the inputs to the network and work out the output. This initial output could be anything, as the initial weights are random numbers.
2. Next work out the error for neuron B. This error is needed actually ErrorB = OutputB (1-OutputB)(TargetB-OutputB) (5.19) Output (1- Output) is necessary in the equation because of the Sigmoid Function (Target – Output) is needed if threshold activation function is used.
3. Change the weight. Let W+AB be the new (trained) weight and WAB being the initial weights. Notice that it is the output of the connecting neuron (neuron A). Update all the weights in the output layer in this way. W+AB= W+AB + ( ErrorB x OutputA) (5.20)
4. Calculate the Errors for the hidden layer neurons. Unlike the output layer it is not possible to calculate these directly (because there is no Target), so Back Propagate them from the output layer (hence the name of the algorithm). This is done by taking the Errors from the output neurons and running them back through the weights to get the hidden layer errors. For example if neuron A is connected as shown to B and C then take the errors from B and C to generate an error for A.
5. Having obtained the Error for the hidden layer neurons now proceed as in stage 3 to change the hidden layer weights. By repeating this method a network can be trained for any number of layers. The Equations (5.19) to (5.21) denotes the calculation of the outputs in the forward propagation

## You May Also Find These Documents Helpful

• Satisfactory Essays

The layers are all about how the application handles data, not the network, which is what the model demonstrates. They could be combined or separated.…

• 505 Words
• 3 Pages
Satisfactory Essays
• Powerful Essays

Rating for the respective network is obtained by substituting the values in the above equation as follows…

• 1393 Words
• 6 Pages
Powerful Essays
• Good Essays

Lastly, attached the Analysis node to each of the golden nugget retrieved, to check the results for the neural network…

• 637 Words
• 3 Pages
Good Essays
• Good Essays

Optimization problems are typically implemented on a feedback network. These networks interconnect the neurons with a feedback path. A typical feedback neural network is the Hopfield neural network [Hop85]. Figure 4 shows the circuit structure of the neuron and its functional structure. This differential equation describes the neuron: [pic]…

• 585 Words
• 3 Pages
Good Essays
• Better Essays

It use the basic principle of convolution. When this matrix multiple x’, every line of this matrix multiple x’ and finally add together.…

• 554 Words
• 3 Pages
Better Essays
• Powerful Essays

Nowlan, S. J., & Hinton, G. E. (1992). Simplifying neural networks by soft weight-sharing. Neural…

• 4443 Words
• 18 Pages
Powerful Essays
• Satisfactory Essays

Every node in this layer is a ﬁxed node labeled Prod. The output is the product of all the incoming signals. Each node represents the ﬁre strength of the rule Any other…

• 1389 Words
• 6 Pages
Satisfactory Essays
• Satisfactory Essays

Training the Spiking Neural Network for the XOR problem with 3bit Integer Weights and Delays 010110100101001101000000100101100100001011010101101001111110000000010110101010010100010001101000001111110101111111100110 111100100111011001111010100111110110001111111101101011111111101001101001101001010000111110110110111011011110110101011000 111011111100110111100101110110010111010110100110101110011001011011000011000000010110101010001111101110110110000101110110 010011010010011111000000011000011110001010110000011100101011001100110001010111111101100010011010100100111000100111010011 110101011000110011010110111100101011111100011011111011010001101011111110000101111111011001010010010100010000010100111100 100110100100010010101010001111011100011001010111011010001101111100110001010111000111110010101110000110001100101100100000 111100110101110100110010100010011000100110100111001101010101000011111111011001011001000100001110100011000110000000001100 100101001011101101111000001001111110101011000001001100110101010011000011100100000011100001111011111010010110000100100000 010010110000001111100101100101111010011010000000000101110010100011001000100100010010010001111000101011100011111110001111 011110011110111010100010011110010100011011101011001011100111010000011111101110111000110010001000100100000100011010010000 101000001110110000110001110111001100001100110100110001010011101011111000101111000100100011010110010001011101011111011101 101110000100111011111010011111100100100100100011100110010111011000011000011100010111010010110001000001101110001110010010 111001100001110111000010110101101000011100101010000101111110101110000111010010001111011000010101111000100111000110011010 110001011010001011100101111111110101110111101101011111011011000100100111110010110101111001000011111010110010010111001110…

• 3707 Words
• 15 Pages
Satisfactory Essays
• Satisfactory Essays

networks and fuzzy logic. The review of neural networks and fuzzy logic is followed by…

• 1374 Words
• 6 Pages
Satisfactory Essays
• Satisfactory Essays

5 Multilayer Feedforward Networks . . . . . . . . . . . . . . . . .…

• 37194 Words
• 149 Pages
Satisfactory Essays
• Powerful Essays

Neural Network is a different paradigm for computing Von Neumann machines which are based on the processing memory abstraction of human information processing. Neural networks are like multiprocessor computer system with simple processing elements, high degree of interconnections, simple scalar messages and adaptive interaction between elements.…

• 1421 Words
• 6 Pages
Powerful Essays
• Powerful Essays

| Calculation of new weights for a back propagation network, given the values of input pattern, output pattern, target output, learning rate and activation function.…

• 4066 Words
• 17 Pages
Powerful Essays
• Good Essays

The vision of making a device which could think like the human mind has always been the part of Science Fiction since time immemorial. In this process the first unforgettable breakthrough came with the concept of ‘The Analytical Engine’ which was developed by Charles Babbage in the mid 19th century. Since then the evolution of computers has taken various leaps. Today in the 21st century we are working with Supercomputer. The moulding of a simple calculator into a Supercomputer has been very startling. But still we have not completely achieved the main objective. This is a reference to Artificial Neural Network which is altogether an emerging field.…

• 689 Words
• 3 Pages
Good Essays
• Good Essays

Feed Forward Network - Information flow is unidirectional, information processing is parallel, memory less, cannot modify output based on error…

• 1608 Words
• 7 Pages
Good Essays
• Powerful Essays

output voltage is measured with respect to the circuit ground node. The model equation for the output…

• 10121 Words
• 41 Pages
Powerful Essays