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Neural networks fundamentals in mobile robot control systems
Оборот титула
Table of contents
1. LECTURE: INTRODUCTION TO NEURAL NETWORKS
+
2. LECTURE: BASES OF LEARNING OF NEURAL NETWORKS
-
2.1. Parametric adaptation of the neural threshold element
2.2. The perceptron rule of adaptation
2.3. Mays adaptation rule
2.4. Adaptive linear element
2.5. α - Least Mean Square Algorithm
2.6. Mean Square Error Method
2.7. μ - Least Mean Square Algorithm
2.8. Adaline with sigmoidal functions
2.9. Backpropagation method
2.10. A simple network with three neurons
2.11. Backpropagation learning
2.12. Problems
Practical training 3
2.13. Task for practical training 3
2.14. Example of the practical training 3 performing
2.15. Variants
2.16. Requirements to the results representation
Practical training 4
2.17. Task for practical training 4
2.18. Example of the practical training 4 performing
2.19. Variants
2.20. Requirements to the results representation
3. LECTURE: MULTILAYERED FEEDFORWARD STATIC NEURAL NETWORKS
+
4. LECTURE: ADVANCED METHODS FOR LEARNING NEURAL NETWORKS
+
BIBLIOGRAPHY
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2. LECTURE: BASES OF LEARNING OF NEURAL NETWORKS
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2.1. Parametric adaptation of the neural threshold element
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Neural networks fundamentals in mobile robot control systems
Table of contents
1. LECTURE: INTRODUCTION TO NEURAL NETWORKS
+
2. LECTURE: BASES OF LEARNING OF NEURAL NETWORKS
-
2.1. Parametric adaptation of the neural threshold element
2.2. The perceptron rule of adaptation
2.3. Mays adaptation rule
2.4. Adaptive linear element
2.5. α - Least Mean Square Algorithm
2.6. Mean Square Error Method
2.7. μ - Least Mean Square Algorithm
2.8. Adaline with sigmoidal functions
2.9. Backpropagation method
2.10. A simple network with three neurons
2.11. Backpropagation learning
2.12. Problems
Practical training 3
2.13. Task for practical training 3
2.14. Example of the practical training 3 performing
2.15. Variants
2.16. Requirements to the results representation
Practical training 4
2.17. Task for practical training 4
2.18. Example of the practical training 4 performing
2.19. Variants
2.20. Requirements to the results representation
3. LECTURE: MULTILAYERED FEEDFORWARD STATIC NEURAL NETWORKS
+
4. LECTURE: ADVANCED METHODS FOR LEARNING NEURAL NETWORKS
+
BIBLIOGRAPHY