Abstract
Soft computing methods of modelling usually include fuzzy logics , neural computation , genetical algorithms and probabilistic deduction , with the addition of data mining and chaos theory in some cases. Unlike the traditional “hardcore methods” of modelling, these new methods allow for the gained results to be incomplete or inexact. Methodologically, the different approaches of these soft methods are quite heterogeneous. Still, all of them have a few things in common, namely that they have all been developed during the last 30–50 years (Bayes formula in 1763 and Lukasiewicz logic in 1920 being the exceptions), and that they would probably have not achieved their current standards without the exceptional growth in computational capacities of computers.
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Notes
- 1.
Fayyad, Piatetsky-Shapiro, Smyth (1996)
- 2.
By data, we mean an m × n matrix whose elements can be any symbols that we want.
- 3.
For our purposes, downloading the 4ft-Miner will be enough.
- 4.
They are also called generalized quantifier.
- 5.
In a wider definition of GUHA and in the LISpMiner software, the cell can also be empty.
- 6.
The name truth based implication is also used.
- 7.
The dilemmas for each and possibly interesting have been solved in GUHA by giving the most compact sentences available as responses.
- 8.
Complete definitions are available in the LISpMiner manual that can be downloaded.
- 9.
The name truth based double implication is also used.
- 10.
This quantifier is also known as truth based equivalence.
- 11.
There is also a below average quantifier in LISpMiner.
- 12.
BL stands for basic logic.
- 13.
MV stands for many valued
- 14.
This system as well as many others have already been build and are widely used in modern traffic signal systems.
- 15.
Compare this to Mamdani and Sugeno type fuzzy deduction machines in the Matlab Fuzzy Logic Toolbox, which require technical and possibly not fully justified particularization methods.
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Turunen, E., Raivio, K., Mantere, T. (2016). Soft Computing Methods. In: Pohjolainen, S. (eds) Mathematical Modelling. Springer, Cham. https://doi.org/10.1007/978-3-319-27836-0_6
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