Learning Advanced Intelligent Computing

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Advanced Intelligent Computing
Published 5/2026
Created by Dr. CH HUSSAIAN BASHA
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 8 Lectures ( 8h 30m ) | Size: 11.4 GB

Neural Networks, Learning Algorithms, and Fuzzy Intelligent Control Systems
What you'll learn
Understand the fundamentals of Artificial Neural Networks (ANNs), including learning networks, neuron models, perceptron learning, delta learning rules, and ada
Apply various neural network models and learning algorithms such as Adaline, Madaline, EBPTA, associative memories, Hamming networks, and Max networks to solve
Analyze and evaluate advanced neural network techniques including Adaptive Resonance Theory (ART), Counter Propagation Networks, coherent networks, and their nu
Demonstrate the concepts of fuzzy sets and fuzzy controllers and apply fuzzy logic techniques for intelligent control applications such as PMSM drive systems.
Requirements
Basic knowledge of Mathematics, especially linear algebra, probability, and calculus
Basic concepts of Control Systems and electrical engineering applications
Fundamental knowledge of Artificial Intelligence
Description
The courseAdvanced Intelligent Computing provides a comprehensive understanding of modern intelligent computational techniques used in artificial intelligence, automation, robotics, and advanced control systems. The course is designed to introduce students to the concepts, architectures, and practical applications of Artificial Neural Networks (ANN), learning algorithms, associative memory models, and fuzzy logic systems. It aims to build a strong foundation in intelligent system design and computational problem-solving techniques widely used in engineering and research applications.
The course begins with the fundamentals of Artificial Neural Networks and their learning mechanisms. Students will study important neural network models such as Perceptron, Adaline, Madaline, and McCulloch-Pitts neuron models. Learning algorithms including Perceptron Learning Rule, Delta Learning Rule, and Evolutionary Back Propagation Training Algorithm (EBPTA) are discussed in detail along with numerical problem-solving approaches to strengthen analytical understanding.
Advanced intelligent network models such as Associative Memories, Coherent Networks, Hamming Networks, Max Networks, Adaptive Resonance Theory (ART), and Counter Propagation Networks are also covered. These topics help students understand pattern recognition, memory storage, adaptive learning, and intelligent decision-making systems used in modern computational applications.
In addition, the course introduces the principles of Fuzzy Sets and Fuzzy Logic, emphasizing their role in handling uncertainty and non-linear systems. Practical applications of fuzzy systems in intelligent control are explored through the design and implementation of Fuzzy Controllers for Permanent Magnet Synchronous Motor (PMSM) drives.
The course combines theoretical concepts, numerical analysis, and application-oriented learning to develop critical thinking, analytical ability, and practical problem-solving skills required for designing advanced intelligent computing systems and smart engineering solutions.
Who this course is for
Undergraduate and postgraduate students in Electrical Engineering, Electronics and Communication Engineering, Computer Science, and related disciplines
Homepage

https://www.udemy.com/course/advanced-intelligent-computing

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