Ardashir Mohammadzadeh – författare

Renewable and Clean Energy Systems Based on Advanced Nanomaterials: Basis, Preparation and Applications describes the fundamental aspects of a diverse range of nanomaterials used in the fields of renewable and clean energy. Various methods of preparing several different nanomaterials for green energy systems, such as advanced nanomaterials for solar cells, mixed metal oxide-based nanomaterials for hydrogen storage, and active nanomaterials for Li ion batteries are presented along with their advantages, disadvantages, and applications. Chapters also discuss novel methods of power analysis, frequency regulation methods, practical applications of solar panels, economic efficiency of solar energy, solar physics, and much more.This is a valuable resource on the basic science, preparation methods, and practical applications of advanced nanomaterials for green energy systems.- Features recent advances on nanomaterials preparation methods and their applications in photovoltaic technology- Discusses sustainable strategies for producing large-scale nanomaterials, focusing on preparation techniques that are cost-effective and eco-friendly- Reviews the efficiency of nanomaterials used in solar energy storage and conversion

Renewable and Clean Energy Systems Based on Advanced Nanomaterials: Basis, Preparation and Applications describes the fundamental aspects of a diverse range of nanomaterials used in the fields of renewable and clean energy. Various methods of preparing several different nanomaterials for green energy systems, such as advanced nanomaterials for solar cells, mixed metal oxide-based nanomaterials for hydrogen storage, and active nanomaterials for Li ion batteries are presented along with their advantages, disadvantages, and applications. Chapters also discuss novel methods of power analysis, frequency regulation methods, practical applications of solar panels, economic efficiency of solar energy, solar physics, and much more.This is a valuable resource on the basic science, preparation methods, and practical applications of advanced nanomaterials for green energy systems.Features recent advances on nanomaterials preparation methods and their applications in photovoltaic technologyDiscusses sustainable strategies for producing large-scale nanomaterials, focusing on preparation techniques that are cost-effective and eco-friendlyReviews the efficiency of nanomaterials used in solar energy storage and conversion

This book explains the basic concepts, theory and applications of fuzzy systems in control in a simple unified approach with clear ex-amples and simulations in the MATLAB programming language. Fuzzy systems, especially, type-2 neuro-fuzzy systems, are now used extensively in various engineering fields for different purposes. In plain language, this book aims to practically explain fuzzy sys-tems and different methods of training and optimizing these systems. For this purpose, type-2 neuro-fuzzy systems are first analyzed along with various methods of training and optimizing these systems through implementation in MATLAB. These systems are then em-ployed to design adaptive fuzzy controllers. The authors aim at pre-senting all the well-known optimization methods clearly and code them in the MATLAB language.

This book explains the basic concepts, theory and applications of fuzzy systems in control in a simple unified approach with clear ex-amples and simulations in the MATLAB programming language. Fuzzy systems, especially, type-2 neuro-fuzzy systems, are now used extensively in various engineering fields for different purposes. In plain language, this book aims to practically explain fuzzy sys-tems and different methods of training and optimizing these systems. For this purpose, type-2 neuro-fuzzy systems are first analyzed along with various methods of training and optimizing these systems through implementation in MATLAB. These systems are then em-ployed to design adaptive fuzzy controllers. The authors aim at pre-senting all the well-known optimization methods clearly and code them in the MATLAB language.

This book provides the fundamental approaches to designing and using type-3 fuzzy systems in real-world applications. Basic Matlab codes are provided to use type-3 fuzzy systems in a straightforward scheme. The main differences between type-3 fuzzy systems and other types are analyzed and compared. The effectiveness of type-3 fuzzy systems is analyzed and studied in various applications, such as robotics, intelligent control systems, and data science.

This book provides the fundamental approaches to designing and using type-3 fuzzy systems in real-world applications. Basic Matlab codes are provided to use type-3 fuzzy systems in a straightforward scheme. The main differences between type-3 fuzzy systems and other types are analyzed and compared. The effectiveness of type-3 fuzzy systems is analyzed and studied in various applications, such as robotics, intelligent control systems, and data science.

A Comprehensive Guide to Theory and Implementation.Bridging the gap between theory and practice with this extensive guide to neural networks, featuring parallel implementations in both Python and MATLAB.Navigating the complex landscape of neural networks requires not only a firm grasp of theoretical foundations but also the practical skills to implement them effectively. Practical Neural Networks in Python and MATLAB is designed to be a definitive resource, offering a unique dual-language approach to mastering these powerful models.Key Features:A Dual-Language, Integrated Approach: This book provides a side-by-side exploration of neural networks in both Python and MATLAB. This methodology allows you to leverage Python's rich deep learning ecosystem (TensorFlow, Keras, PyTorch) and MATLAB's specialized toolboxes, giving you the flexibility to work within your preferred environment or across different project requirements.Comprehensive Coverage of Algorithms and Architectures: Move beyond basic backpropagation. The text provides a systematic review of fundamental and advanced training algorithms, including Gradient Descent, Newton's Method, Levenberg-Marquardt, Recursive Least Squares (RLS), and metaheuristics like Genetic Algorithms and Particle Swarm Optimization. Furthermore, it offers a detailed survey of over 25 major neural network architectures, from foundational Perceptrons and Feedforward Networks to advanced systems like CNNs, RNNs (LSTM, GRU), Autoencoders, GANs, and Deep Belief Networks.Practical, Code-Oriented Learning: Each concept and architecture is accompanied by ready-to-run code examples. This practical focus ensures that you can immediately translate theoretical understanding into functional code, experiment with parameters, and adapt the implementations to your own unique challenges.Real-World Application and Case Studies: The learning is grounded in practicality through diverse case studies across multiple domains. You will find applications in medical diagnostics (e.g., diabetes classification), time-series forecasting (e.g., air quality prediction), system identification, natural language processing, and more. These examples provide complete pipelines from data preprocessing and model training to evaluation and visualization.This Book is Ideal For:University students and researchers in Computer Science, Artificial Intelligence, Engineering, and related fields.R&D engineers and scientists working in algorithm development, data analysis, and intelligent systems.Any practitioner seeking a thorough, hands-on understanding of neural networks with the flexibility to work in both Python and MATLAB environments.In essence, Practical Neural Networks in Python and MATLAB serves as an invaluable companion for anyone looking to deepen their expertise in neural networks. It is more than a textbook; it is a practical toolkit designed to accelerate your research, enhance your projects, and provide a clear, comprehensive reference for the key architectures and algorithms shaping the field of AI today.