ABSTRACT
Integrated circuits based on fuzzy logic are used in expert systems in the fields of command and control for real-time operations. The soft computing concept was developed to exploit tolerance for inaccuracy, uncertainty, and partial truth in order to gain flexibility, robustness, low cost of solutions, and a better connection with reality. This feature makes it different from conventional (hard) computing, which is characterized by a lack of inaccuracy and partial truths. The ultimate goal would be to match or even surpass the performance of the human mind. Soft computing is characterized by a partnership of several domains, the most important of which are neural networks, genetic algorithms, fuzzy logic, and probabilistic reasoning. Based on the human thinking model, soft computing brings these areas together in a complementary rather than competitive relationship, in which each partner contributes its own advantages and techniques to solve problems that are impossible to solve in another mode. Located between artificial intelligence systems and conventional computing, soft computing is the problem to be solved in such a way that the current state of the system can be measured and compared with the state of the needle to be obtained. The state of the system is the basis for adapting the parameters, which converge with each step toward the optimal solution.
