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Electric Circuits, Projects
Electrical circuits analysis
MATLAB is a numeric computation software for engineering and scientific calculations. MATLAB is increasingly being used by students, researchers, practicing engineers and technicians. The causes of MATLAB popularity are
legion. Among them are its iterative mode of operation, built-in functions, simple programming, rich set of graphing facilities, possibilities for writing additional functions, and its extensive tool boxes.SKU: n/a
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Artificial Neural Networks, Machine Learning, Projects
Artificial neural networks to passive acoustical identification of animals
Artificial Neural Networks, Machine Learning, ProjectsArtificial neural networks to passive acoustical identification of animals
In this project, MATLAB codes have been developed by MATLAB4Engineers team. Algorithms such as Extreme Learning Machine (ELM), Multi-Layer Perceptron (MLP), and different strategies for features extraction has been implemented and tested in MATLAB.
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Energy Systems, Projects, Renewable Energy, Solar Systems
MODELING AND SIMULATION OF PV SOLAR SYSTEM
Photovoltaic solar power technology is being pushed to the forefront as a viable alternative source of renewable energy. Addressing the key design challenges involved in the development and implementation of these systems requires simulation tools capable of not only modeling and analyzing the behavior of diverse electronic components but also the ability to combine them with the associated control algorithms and evaluate the full system performance.
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Books, Machine Learning, Machine Learning
Machine Learning Book
This book focuses on data analytics and machine learning techniques in MATLAB using functionality within Statistics and Machine Learning Toolbox and Neural Network Toolbox. The book demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results.
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Projects, Signal Processing
ADAPTIVE LINEAR ELEMENT (ADALINE) – SIGNAL PROCESSING
The ADALINE (adaptive linear neuron) networks applied in this project are similar to the perceptron, but their transfer function is linear rather than hard-limiting. This allows their outputs to take on any value, whereas the perceptron output is limited to either 0 or 1. Both the ADALINE and the perceptron can solve only linearly separable problems. However, here the LMS (least mean squares) learning the rule, which is much more powerful than the perceptron learning rule, is used. The LMS, or Widrow-Hoff, learning rule minimizes the mean square error and thus moves the decision boundaries as far as it can from the training patterns.
In this project, you design an adaptive linear system that responds to changes in its environment as it is operating. Linear networks that are adjusted at each time step based on new input and target vectors can find weights and biases that minimize the network’s sum-squared error for recent input and target vectors. Networks of this sort are often used in error cancellation, signal processing, and control systems.SKU: n/a
