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mplement the function given in the table below using perceptron networks for bipolar inputs and targets. Take the learning rate to be 1. -1 -1 -1 -1 1 -1 -1 1 1.

mplement the function given in the table below using perceptron networks for bipolar inputs and targets. Take the learning rate to be 1. -1 -1 -1 -1 1 -1 -1 1 1.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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### Implementing Perceptron Networks with Bipolar Inputs and Targets In this tutorial, we will demonstrate how to implement the function provided in the table below using perceptron networks with bipolar inputs (i.e., inputs that are either -1 or 1) and targets. For this exercise, we will consider the learning rate (η) to be 1. The table illustrates the set of inputs \((x_1, x_2)\) and their corresponding output values (y): | \(x_1\) | \(x_2\) | y | |:------:|:------:|:--:| | -1 | -1 | -1 | | -1 | 1 | -1 | | 1 | -1 | 1 | | 1 | 1 | 1 | To implement this function using a perceptron network, we need to follow these steps: 1. **Initialize Weights and Bias:** Set initial weights and bias values. 2. **Apply Inputs to the Network:** Present each input set \((x_1, x_2)\) to the perceptron. 3. **Calculate Output:** Compute the output using the weighted sum and apply the activation function to determine the final output. 4. **Update Weights and Bias:** Modify the weights and bias according to the error obtained if the output does not match the target value. 5. **Iterate:** Repeat the steps above for a number of epochs or until the output aligns perfectly with the target values for all input sets. We will go through each step using the given table and perceptron learning rule, ultimately deriving the weights and bias that allow the perceptron network to correctly classify the inputs. This will show the perceptron's ability to implement basic logical functions, forming the foundation for more complex neural networks.
Transcribed Image Text:### Implementing Perceptron Networks with Bipolar Inputs and Targets In this tutorial, we will demonstrate how to implement the function provided in the table below using perceptron networks with bipolar inputs (i.e., inputs that are either -1 or 1) and targets. For this exercise, we will consider the learning rate (η) to be 1. The table illustrates the set of inputs \((x_1, x_2)\) and their corresponding output values (y): | \(x_1\) | \(x_2\) | y | |:------:|:------:|:--:| | -1 | -1 | -1 | | -1 | 1 | -1 | | 1 | -1 | 1 | | 1 | 1 | 1 | To implement this function using a perceptron network, we need to follow these steps: 1. **Initialize Weights and Bias:** Set initial weights and bias values. 2. **Apply Inputs to the Network:** Present each input set \((x_1, x_2)\) to the perceptron. 3. **Calculate Output:** Compute the output using the weighted sum and apply the activation function to determine the final output. 4. **Update Weights and Bias:** Modify the weights and bias according to the error obtained if the output does not match the target value. 5. **Iterate:** Repeat the steps above for a number of epochs or until the output aligns perfectly with the target values for all input sets. We will go through each step using the given table and perceptron learning rule, ultimately deriving the weights and bias that allow the perceptron network to correctly classify the inputs. This will show the perceptron's ability to implement basic logical functions, forming the foundation for more complex neural networks.
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