Soft Computing

Introduction:

In computer science, soft computing is the use of inexact solutions to computationally hard tasks such as the solution of NP-complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind.

Objective:

Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost. As such it forms the basis of a considerable amount of machine learning techniques. Recent trends tend to involve evolutionary and swarm intelligence based algorithms and bio-inspired computation.

Equipments:

  • Hardware:
  • CPU: Pentium Dual Core E5400 @2.70GHz
  • RAM: DDR3 2 GB
  • OS: Windows 7 Ultimate (32 Bit)
  • HDD: 320 GB
  • Software:
    • Matrix Laboratory (MATLAB) v8.4 R2014b
    • Bundled Java Virtual Machine (JVM) v1.7.0_11

Industrial Prospect:

Soft Computing has an important consequence: in many cases a problem can be solved most effectively in combination rather than exclusively. Such systems are becoming increasingly visible like in:

  • Fuzzy Systems
  • Bioinformatics and Biomedicine
  • Neural Networks
  • Evolutionary Computation
  • Machine Learning
  • Probabilistic Reasoning