文档介绍：An Introduction to
Practical works and ic Algorithms
For Engineers and Scientists
Christopher MacLeod
The author retains copyright of
this material.
Contents
PART A – works
1. An introduction to works
2. Artificial works
3. The Back Propagation Algorithm
4. Some illustrative applications feed works
5. Pre-processing input data
6. Network layers and size
7. Hopfield and works
8. works
9. Neurons with time dependant outputs
10. Implementing s
PART B – Evolutionary Algorithms
11. An introduction to evolution
12. The ic Algorithm
13. Some applications of ic Algorithms
14. Additions to the basic GA
15. Applying ic Algorithms to works
16. Evolutionary Strategies and Evolutionary Programming
17. Evolutionary Algorithms in advanced AI systems
Appendix A – Tips for programmers.
1. An introduction to works
The Artificial work ( or just ANN for short) is a collection of
simple processors connected together. Each processor can only perform a very
straightforward mathematical task, but a work of them has much greater
capabilities and can do many things which one on its own can’t. Figure , shows the
basic idea.
Figure , a consists of many simple processing units connected together.
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Simple processing Neurons are connected together to
unit (neuron) form work
The inspiration behind the is the brain. The human brain consists of about
100 billion processing units connected together in just such work. These
processing units are called “Brain Cells” or “Neurons” and each one is a living cell.
Before proceeding to discuss the operation of the Artificial work, it will
help us if we pause to understand something of how the real one works.
Real Brains
Looking at a real neuron under a microscope (it’s much too sm