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Neural networks fundamentals in mobile robot control systems
4. LECTURE: ADVANCED METHODS FOR LEARNING NEURAL NETWORKS
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Table of contents
1. LECTURE: INTRODUCTION TO NEURAL NETWORKS
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2. LECTURE: BASES OF LEARNING OF NEURAL NETWORKS
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3. LECTURE: MULTILAYERED FEEDFORWARD STATIC NEURAL NETWORKS
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4. LECTURE: ADVANCED METHODS FOR LEARNING NEURAL NETWORKS
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4.1. Different Criteria for Error Measure
4.2. Complexities in Regularization
4.3. Weight Decay Approach
4.4. Weight Elimination Approach
4.5. Chauvin's Penalty Approach
4.6. Network Pruning Through Sensitivity Calculation
4.7. Karnin's Pruning Method
4.8. Optimal Brain Damage
4.9. Calculation of the Hessian Matrix
4.10. Second-order Optimization Learning Algorithms
4.11. Recursive Estimation Learning Algorithms
4.12. Tapped Delay Line Neural Networks
4.13. Applications of TDLNN for Adaptive Control Systems
4.14. Problems
Practical training 7
4.15. Task for practical training 7
4.16. Example of the practical training 7 performing
4.17. Variants
4.18. Requirements to the results representation
BIBLIOGRAPHY
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