Have you heard about soft computing? This is also a computing technique but differs from standard (hard) computing in that, unlike hard computing, it’s tolerant of imprecision, partial truth, uncertainty and approximation. In effect, the model for soft computing is the human mind.
The main constituents of soft Computing are:
- Neural Networks
- Fuzzy logic
- Organic process
- Computation, Bayesian Network
- Swarm Intelligence.
The successful applications of soft computing recommend that the impact of soft computing are going to be felt more and more in recent years. Soft computing is likely to play a very important role in science and engineering. However eventually its influence might extend much farther.
What is vital to notice is that soft computing isn’t a melange. Rather, it’s a partnership within which each of the partners contributes a distinct methodology for addressing issues in its domain. In this context, the principal constituent methodologies in SC are complementary instead of competitive. Furthermore, soft computing is also viewed as a foundation element for the emerging field of conceptual intelligence.
The complementarity of FL, NC, GC, and PR has a very important consequence: in some cases a problem can be resolved most effectively by using FL, NC, GC and PR together instead of exclusively. A striking example of a very effective combination is what has come to be referred to as “neurofuzzy systems”. Such systems are getting more and more visible as client products starting from air conditioners and washing machines to photocopiers and camcorders.
Less visible but perhaps even a lot of necessary are neurofuzzy systems in industrial applications. What’s significantly important is that in both client products and industrial systems. The utilization of soft computing techniques leads to systems that have high MIQ (Machine Intelligence Quotient). In large measure, it’s the high MIQ of SC-based systems that accounts for the rapid growth in the number and type of applications of soft computing.
Soft computing encompasses a collection of computational techniques and algorithms that are used to deal with complicated systems. Soft computing exploits the given tolerance for imprecision, partial truth, and uncertainty for a specific problem. In many ways, soft computing represents a major paradigm shift in the aims of computing – a shift that reflects the actual fact that the human mind, unlike present day computers, possesses a stimulating ability to store and process info that is pervasively uncertain, imprecise and lacking in categoricity.0