Embracing Uncertainty and Approximations for Intelligent Problem-Solving with Soft Computing

Unlike traditional computing, soft computing solves difficult real-world problems and works with approximation models. Soft computing is more forgiving of approximations, ambiguity, partial truth, and imprecision than hard computing. The human mind is, in a sense, the ideal model for soft computing. Techniques like fuzzy logic, machine learning, genetic algorithms, expert systems, and artificial neural networks are the foundation of soft computing. Even though soft computing theory and methods were initially presented in the 1980s, automatic control engineering currently uses them as a major area of research and study. Soft computing techniques are currently being successfully applied in a wide range of commercial, industrial, and household applications. Soft computing techniques and application areas are expected to grow further with the introduction of low-cost, outstanding-performance digital processing units and the falling cost of memory chips. The purpose of this paper is to discuss embracing uncertainties and approximation for intelligent problem-solving with the help of soft computing. First, the study has discussed various soft computing systems. Moreover, the concept of fuzzy logic has been illustrated along with a description of its applications. In terms of research methodology, this research paper has followed a qualitative research approach by gathering knowledge based on reviewing a wide range of different studies regarding the research topic. According to the findings of this research, as computer processing units get cheaper and more powerful, intelligent systems and soft computing techniques become more significant. Complex algorithms must be used by intelligent systems to make difficult judgments and select the optimal solution from a range of options.