Название: Neural Networks, Machine Learning, and Image Processing: Mathematical Modeling and Applications
Автор: Manoj Sahni, Ritu Sahni
Издательство: CRC Press
Год: 2022
Страниц: 221
Язык: английский
Формат: pdf (true)
Размер: 16.5 MB
Mathematical modeling is a field that provides fresh insights into natural phenomena by approximating and formulating physical situations. Scientists gather real-world data relevant to a specific topic through observations or experiments and then develop mathematical models to explain and predict the behavior of the real-world object whose scientific model they created. These models are close representations of real objects, not exact replicas. Thus, it is essential to work on the development of more precise models by using various mathematical tools. Mathematical modeling becomes easier with the help of machine learning tools and neural network algorithms. Neural network algorithms, in fact, work in the same way that our brains do. We begin by observing any real-life phenomenon with our eyes or collecting data with machines such as microscopes, telescopes, and cameras, and then we process that data by hypothesizing the underlying principles hidden in the phenomenon. The neural network also receives inputs in the form of numerical data, text, images, or any type of pattern, then processes the inputs by translating those data through various algorithms, and finally generates outputs. The output was evaluated by using Simulink/MATLAB software.
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