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- The purpose of life.
- Back propagate
- A way of processing data in a network, going from the output to the input.
- Delta Value
- A local gradient between the actual output and the desired output in a neuron. See
Chapter 4.2 and
4.3 for a more detailed description.
- Forward propagate
- A way of processing data in a network, going from the input to the output.
- Gnuplot
- A simple console based program for plotting graphs under both Linux and Windows.
- Hidden layer
- The layer not visible from outside the network.
- Input layer
- The input layer of neural network, it does not contain any active neurons.
- Kohonen's SOFM
- A specific type of SOFM. See
Chapter 2.1 for a more detailed description.
- Lambda
-
A neighbourhood function, that computes a value proportional to the distance. The value is large at small distances and small at far distances.
- Layer
- A part of a network of neurons.
- Learning rate
- A parameter to decide how quick the network should be able to learn.
- Momentum
- A parameter used to overcome local minima.
- Network
- In this report a system of neurons.
- Neuron
- The building blocks containing all intelligent in the brain. See
Chapter 4.1 for a more detailed description.
- Output layer
- The layer of neurons that gives the output data.
- Overtraining
- A behaviour that accurse if the network is trained to much. See Chapter 6.2 for a more detailed description.
- Processor cache
- An internal very fast and expensive memory in a processor.
- RMS error
- A way of calculating a type of mean error.
- RISC-computer architecture
-
A type of processor architecture, the one used in PC’s.
- Sigma
-
A function used in the lambda neighbourhood function and it is an approximation of the normal distribution.
- SOFM
- Self organization feature maps. See
Chapter 2.1 for a more detailed description.
- Supervised Learning
-
A type of training when an input and the correct output are given to a network.
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