Introducing SC
Over the past few decades, many researchers have developed numerous methods and systems, many of which have been successfully used in real-world applications. In these applications, most of the methods are based on probabilistic paradigms, such as the well-known Bayesian inference, rules of thumb, and decision systems. Since the 1960s, several great and epoch-making theories have been proposed using fuzzy logic, genetic algorithms, evolutionary computation, and neural networks – all methods that are referred to as SC. When combined with well-established approaches to probability, these new SC methods become effective and powerful in real-world applications. The ability of these techniques to include inaccuracy and incompleteness of information and model very complex systems makes them useful tools in many sectors.
SC takes on the following characteristics:
- The ability to model and control uncertain and complex systems, as well as to represent knowledge...
