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IEEE/ICACT20240199 Slide.07        [Big Slide]	Oral Presentation
	Artificial Neural Networks, including the single-layered perceptron, are well-suited for tasks involving noisy and complex sensor data. ANN learning is robust to errors in the training data, making it an ideal choice for scenarios where data quality might be compromised. The ability to learn from noisy data is particularly beneficial in applications such as sensor fault detection in Wireless Sensor Networks (WSN). The single-layered perceptron in an Artificial Neural Network utilizes the backpropagation algorithm for learning. Backpropagation employs gradient descent, a technique to fine-tune network parameters based on the sum squared training errors. The goal of the gradient descent is to minimize the cost function by iteratively updating the weights in the direction that reduces the error between predicted and target values. This iterative process continues until the algorithm converges to a set of weights that optimally fit the training set.

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