Consider an industrial automation and control application. It has a normalized bi-folded dataset comprises of pressure...

 

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Consider an industrial automation and control application. It has a normalized bi-folded dataset comprises of pressure and temperature, as shown. The model has a corresponding bi-state output ('1' motor on, '0' motor off). A Single Layer Perceptron (SLP) is required to be designed to classify a two-dimensional data. You are given with the following four patterns (p) along with their target classes (t). Output (t) Input (x) x x t -1 -1 1 -1 1 0 -1 0 1 Given that the perceptron has the following initial weights, learning rate, and threshold, respectively. Wij= [1 0.2], a=0.1, and 0=-0.25. a. Construct the neural schema for SLP-ANN with labeled weights and neurons. Also plot the patterns in a two-dimensional space with the decision boundary. b. Compute the actual output, error, correct percentage recognition, and trained weights for ONE epochs ONLY. Consider an industrial automation and control application. It has a normalized bi-folded dataset comprises of pressure and temperature, as shown. The model has a corresponding bi-state output ('1' motor on, '0' motor off). A Single Layer Perceptron (SLP) is required to be designed to classify a two-dimensional data. You are given with the following four patterns (p) along with their target classes (t). Output (t) Input (x) x x t -1 -1 1 -1 1 0 -1 0 1 Given that the perceptron has the following initial weights, learning rate, and threshold, respectively. Wij= [1 0.2], a=0.1, and 0=-0.25. a. Construct the neural schema for SLP-ANN with labeled weights and neurons. Also plot the patterns in a two-dimensional space with the decision boundary. b. Compute the actual output, error, correct percentage recognition, and trained weights for ONE epochs ONLY.