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Soft computing is the reverse of hard (conventional) computing. It refers to a group of computational techniques that are based on artificial intelligence (AI) and natural selection. It provides cost-effective solutions to the complex real-life problems for which hard computing solution does not exist.
Feb 22, 2023 · Soft Computing: Soft computing, on the other hand, uses techniques such as fuzzy logic, neural networks, genetic algorithms, and other heuristic methods to solve problems. It is based on the idea of approximation and is ideal for solving problems that are difficult or impossible to solve exactly.
Feb 14, 2023 · Soft computing is a branch of artificial intelligence that provides approximate solutions to complex problems that are difficult or impossible to solve using classical methods. The following are some of the reasons why soft computing is needed:
Soft computing is an umbrella term used to describe types of algorithms that produce approximate solutions to unsolvable high-level problems in computer science. Typically, traditional hard-computing algorithms heavily rely on concrete data and mathematical models to produce solutions to problems.
Soft computing is a kind of computing technology that we use to develop intelligent machines. These machines have human-like problem-solving capabilities. Later they offer imprecise, but useful solutions for problems that are complex and even computational.
Here in our article, we are mainly focusing on soft computing, its techniques like fuzzy logic, artificial neural network, genetic algorithm, comparison between hard computing and soft computing, soft computing techniques, applications, and advantages.
Soft computing is an alternative computing methodology that combines neural networks, fuzzy logic, and genetic algorithms. It is used in artificial intelligence applications where finding a quick approximate solution to a loosely defined problem is more important than finding an exact solution.
