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Breakout Prediction in Continuous Caster by Artificial Neural Networks
| Before we begin |
Before we begin studying the implementation, let us see if we have to because as it's said in A guide to neural Computing applications about neural networks:
''It is easy to be carried away and begin to overestimate their capabilities. The usual consequence of this is, hopefully, no more serious than an embarrassing failure with concomitant mutterings about black boxes and excessive hype. Neural networks cannot solve every problem. Traditionnal methods may be better.''
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| Who is concerned ? |
From: NN FAQ - Who is concerned with NN ?
''Neural Networks are interesting for quite a lot of very different people:
- Computer scientists want to find out about the properties of non-symbolic information processing with neural nets and about learning systems in general.
- Statisticians use neural nets as flexible, nonlinear regression and classification models.
- Cognitive scientists view neural networks as a possible apparatus to describe models of thinking and consciousness (High-level brain function).
- Neuro-physiologists use neural networks to describe and explore medium-level brain function (e.g. memory, sensory system, motorics).
- Physicists use neural networks to model phenomena in statistical mechanics and for a lot of other tasks.
- Biologists use Neural Networks to interpret nucleotide sequences.
- Philosophers and some other people may also be interested in Neural Networks for various reasons.
- Engineers of many kinds exploit the capabilities of neural networks in many areas, such as signal processing and automatic control.''
We obviously belong to the last categorie.
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| The issues |
From Introduction to the Theory of Neural Computation, page 8
''Massive parallelism in computationnal networks is extremly attractive in principle. But in practice there are many issues to be decided before a successful implementation can be achieved for a given problem:
## What is the best architecture? Should the units be divided into layers, or not? How many connections should be made between units, and how should they be organized? What sort of activation functions should be used? What type of updating should be used: synchonous or asynchronous, deterministic or stochastic? How many units are needed for a given task?
## How can a network be programmed? Can it learn a task or must it be predesigned? If it can learn a task, how how many examples are needed for a good performance? How many times must it go through the examples? Does it need the right answers during training, or can it learn from correct/incorrect reinforcement? Can it learn in real-time while functioning, or must the training phase be separated from he performance phase?
## What can the various types of network do? How many different tasks can they learn? How well? How fast? How robust are they to missing information, incorrect data, and unit removal or malfunction? Can they generalize from known tasks or examples to unknown ones? What classes of input-to-output functions can they represent?''
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| The answers |
Err... well, coming soon.
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