Soft computing and hard computing are two types of computing methods. Hard computing is the traditional methodology that is based on the principles of accuracy, certainty, and rigidity. Soft computing on the other hand is a modern approach based on the concept of approximation, uncertainty, and flexibility. Let us discuss some more **Differences between soft computing and hard computing** with the help of the comparison given below.

**What is Soft computing?**

Soft computing is a computing model that was developed to solve non-linear problems that involve uncertain, imprecise, and approximate solutions. These are considered real-life problems that require human-like intelligence to solve. Dr. Lotfi Zadeh coined the term soft computing, which he defines as an approach that imitates the human mind to reason and learn in an environment of uncertainty and impression.

**What is Hard computing?**

Hard computing is the traditional approach to computing that requires a precisely stated analytical model. Dr, Lotfi Zadeh proposed it before soft computing. Using a mathematical model or algorithm, the hard computing approach produces a guaranteed, deterministic, accurate result and defined definite control actions. It works with binary and crisp logic, which require exact input data in sequential order. Hard computing, on the other hand, is incapable of solving real-world problems in which the behaviors are extremely imprecise and the information changes on a regular basis.

**Soft computing Vs Hard computing | Difference between soft computing and hard computing:.**

- The soft computing model is imprecision tolerant, partial truth, and approximation. On the other hand, hard computing does not work on the above-given principle, it is very accurate and certain.
- Soft computing employs fuzzy logic and probabilistic reasoning while hard computing is based on binary or crisp systems.
- The soft computing approach is probabilistic in nature whereas hard computing is deterministic.
- Hard computing has features such as precision and categoricity. , approximation and dispositionalism are the characteristics of soft computing.
- Parallel computations can be performed in soft computing. On the contrary hard computing, sequential computation is performed on the data.
- Soft computing can be easily operated on noisy and ambiguous data. Hard computing, on the other hand, can only work with exact input data.
- Soft computing, which has human-like intelligence and can solve real-life problems, resolves non-linear issues involving uncertainty and impreciseness. Hard computing works best for solving mathematical problems with precise answers.
- Hard computing takes a lot of time in computing as it requires the state's analytical model and model soft computing is based on that of human intelligence.
- Soft computing can handle a large amount of data and multiple computations that may or may not be exact in parallel. Hard computing necessitates precise data input and is sequential.
- Because soft computing is stochastic in nature, it is better suited to solving real-world problems. It is a randomly defined process that can be statistically analyzed but not precisely. While hard computing is best for solving mathematical problems, it does not solve real-world problems.

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