| Model Browser User's Guide | |
Neural Networks
For help on the neural net models implemented in the MBC Toolbox, see the documentation in the Neural Network Toolbox. At the MATLAB command line, enter
The training algorithms available in MBC are traingdm, trainlm, and trainbr.
These algorithms are a subset of the ones available in the Neural Network Toolbox. (The names indicate the type: gradient with momentum, named after the two authors, and bayesian reduction). Neural networks are inspired by biology, and attempt to emulate learning processes in the brain.
Neural nets contain no preconceptions of what the model shape will be, so they are ideal for cases with low system knowledge. They are useful for functional prediction and system modeling where the physical processes are not understood or are highly complex.
The disadvantage of neural nets is that they require a lot of data to give good confidence in the results, so they are not suitable for small data sets. Also, with higher numbers of inputs, the number of connections and hence the complexity increase rapidly.
MBC provides an interface to some of the neural network capability of the Neural Network Toolbox. Therefore these functions are only available if the Neural Network Toolbox is installed. See the Neural Network Toolbox documentation for more help.
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