Hands On Neural Network Applications In Control Systems For Real World Robotics And Sensor Tech Practical Guide For Engineers And Makers Looking To Optimize Automation And Smart Control

Hands-On Neural Network for Real World Robotics Control
✔️ Used book, 309 pages, English, released 1995, control-focused guide

This book explains how neural networks apply to control systems and real world robotics, providing practical context for engineers and makers building automation and sensor-based solutions.

✅ Covers Kohonen nets, n-tuple nets, radial basis function networks, and MLP backpropagation
✅ Includes basic principles, design features, and practical implementation tips
✅ Real-world application studies across control systems, robotics, and sensors
✅ Aimed at engineers and makers with a control or systems background

💡 What is a handy long-tail question about applying neural networks to control systems in real world robotics and automation?

This guide provides practical answers for engineers and makers looking to optimize automation with neural nets. - practical examples in robotics control and sensor systems - guidance on network types like Kohonen, radial basis, and MLP for control - tips for implementing control solutions with real world constraints✝️