Tahir Cetin Akinci – författare

Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide on the intricacies of AI models and their real-world applications. The book demystifies complex AI methodologies by providing clear explanations and practical examples that are reinforced with Python and MATLAB program codes. Its content structure emphasizes a practical, applications-driven approach to understanding AI models, with hands-on coding examples throughout each chapter. Readers will find the tools they need to build AI models, along with the knowledge to make these models accessible and interpretable to stakeholders, thus fostering trust and reliability in AI systems.As the primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results, and bias (data and algorithm) management, this resource give researchers and developers what they need to be able to not only implement AI models, but also interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable. - Covers the fundamental concepts needed to develop various types of Artificial Intelligence models- Includes MATLAB and Python code, allowing readers to directly implement AI models and see their applications in real-world scenarios- Each AI model is thoroughly explained, with a focus on making complex concepts accessible to both beginners and advanced users- Presents case studies from various industries, demonstrating how AI models can be effectively applied and interpreted in different contexts

In an age where intelligent systems are transforming engineering practice, Artificial Neural Networks for System Identification and Control offers a clear roadmap to mastering artificial intelligence (AI) driven modeling and control. From mathematical neuron models to adaptive Artificial Neural Network (ANN) based controllers, this book combines theory, algorithms, and hands on coding to help readers design and analyze intelligent systems. Rich with visual examples and real world case studies, it demonstrates how neural networks outperform traditional control methods in handling nonlinearity, uncertainty, and dynamic system behavior.* Offers a practical and accessible guide to ANN based system identification and control* Blends mathematical insight with real engineering applications* Provides Python supported examples and visual case studies* Highlights key advances in nonlinear modeling and adaptive control design* Bridges the gap between theory, simulation, and real world deploymentThis book is intended for engineers, researchers, and advanced students seeking to apply artificial intelligence to control theory, robotics, and signal processing and to design smarter, more adaptive engineering systems.

In an age where intelligent systems are transforming engineering practice, Artificial Neural Networks for System Identification and Control offers a clear roadmap to mastering artificial intelligence (AI) driven modeling and control. From mathematical neuron models to adaptive Artificial Neural Network (ANN) based controllers, this book combines theory, algorithms, and hands on coding to help readers design and analyze intelligent systems. Rich with visual examples and real world case studies, it demonstrates how neural networks outperform traditional control methods in handling nonlinearity, uncertainty, and dynamic system behavior.* Offers a practical and accessible guide to ANN based system identification and control* Blends mathematical insight with real engineering applications* Provides Python supported examples and visual case studies* Highlights key advances in nonlinear modeling and adaptive control design* Bridges the gap between theory, simulation, and real world deploymentThis book is intended for engineers, researchers, and advanced students seeking to apply artificial intelligence to control theory, robotics, and signal processing and to design smarter, more adaptive engineering systems.