Soft Computing

Soft Computing

Soft Computing is the use of estimated calculation to provide an impossible but useful solution for complex computational problems. The approach enables solutions to those problems that may either be unwanted or may be too time consuming to resolve with the current hardware. Soft computing is sometimes known as computational intelligence.

Soft computing provides an approach to problem-solving by using tools other than computers. With the human brain as a role model, soft computing is contrary to partial computing model, tolerant to partial truth, uncertainty, malfunction and approximation. The tolerance of soft computing allows researchers to solve some problems that can not be used in conventional computing.

Soft computing uses component components of study:

• Fuzzy logic
• Machine learning
• Perceptron
• Genetic algorithms
• Differential algorithms
• Support vector machines
• Metaheuristics
• Swarm intelligence
• Probabilistic reasoning
• Evolutionary computation
• Ant colony optimization
• Particle optimization
• Bayesian networks
• Artificial neural networks
• Expert systems

As a field of mathematical and computer study, soft computing has been around the 1990s. Motivation was the capability of the human mind to prepare solutions for real-world through estimates. Unlike soft computing prospects, an approach that is used when there is not enough information available to solve any problem. On the contrary, soft computing is used where the problem is not adequately specified for the use of traditional mathematics and computer technologies. There are many real-world applications in soft, computing, domestic, commercial and industrial situations.