Soft computing
From Free net encyclopedia
Template:Expert Soft Computing refers to a collection of new computational techniques in computer science, artificial intelligence, machine learning, and many applied and engineering areas where one tries to study, model, and analyze very complex phenomena that more precise scientific tools of the past were not quite capable of their low cost, analytic, and complete solution. Scientific methods of previous centuries could model, and precisely analyse, merely, relatively simple systems of physics, classical Newtonian mechanics, and engineering. More complex cases such as systems related to biology and medicine, humanities, management sciences, and similar fields remained outside of the main territory of successful applications of precise mathematical, and analytical methods.
As the most important components of soft computing on can list:
- Neural networks (NN)
- Fuzzy systems (FS)
- Evolutionary computation, including: (EC)
- Ideas about probability including:
- Chaos theory
Generally speaking soft computing techniques resemble human reasoning more closely than traditional techniques which are largely based on conventional logical systems, such as sentential logic and predicate logic, or rely heavily on the mathematical capabilities of a computer. Soft computing techniques are often used to complement each other in applications. It should be pointed out that simplicity and complexity of systems are relative, and certainly, most successful mathematical modelings of the past have also been challenging and very significant.
Soft computing, as opposed to traditional hard and exact computing techniques, could be considered as outcomes of new scientific efforts towards modeling, analysis, and control of complex systems more readily and successfully.
Unlike hard computing schemes that strive for exactness and for full truth, soft computing techniques are tolerant of imprecision, partial truth, and uncertainty. They are mainly, similar to those methods that are applied by human mind in order to reach decisions, and conclusions. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost.
