Soft Computing Methods
Research output: Chapter in Book/Report/Conference proceeding › Chapter › Scientific › peer-review
|Title of host publication||Mathematical Modelling|
|Publisher||Springer International Publishing|
|Number of pages||34|
|Publication status||Published - 2016|
|Publication type||A3 Part of a book or another research book|
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.