Soft Computing
Soft Computing is a computing model developed to address non-linear problems which require uncertain, imprecise and approximate solutions. These types of problems are considered real-world problems where human-like intelligence is required for the resolution. This approach utilises fuzzy logic, artificial neural networks, evolutionary algorithms, probabilistic methods and other methods which can provide approximate solutions to real-world problems.
Hard Computing
Hard Computing is the traditional approach used in computing which requires an accurately specified analytical model. The output of the hard computing approach is a guaranteed, exact and correct result and defines specific control actions using a mathematical model or algorithm. It deals with binary and crisp logic which require precise input data in sequence. Hard computing is not capable of solving real-world problem’s solutions.
Difference between Soft Computing and Hard Computing:
Soft Computing:
1. Soft computing is based on inexact solutions and approximate reasoning.
2. It is well suited for problems where exact solutions are difficult or impossible to achieve.
3. Soft computing usually involves techniques such as fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning.
4. It allows for imprecision, uncertainty, and partial truth to be expressed in the solutions.
Hard Computing:
1. Hard computing is based on exact solutions and precise reasoning.
2. It is well suited for problems where exact solutions can be easily achieved.
3. Hard computing usually involves techniques such as rule-based systems, logic programming, and decision trees.
4. It requires precise solutions and exact reasoning.