# Difference Between Soft Computing and Hard Computing

Hard computing is the conventional approach that is used in computing and needs accurately stated analytical model. Soft computing is the reverse of conventional computing that aims to provide approximation and quick solutions to find quick solutions to complex real-life problems.

In this article, we will be discussing the difference between soft computing and hard computing.

### Table of Contents

- Difference Between Soft Computing and Hard Computing
- What is soft computing?
- What is hard computing?

**Difference Between Soft Computing and Hard Computing**

Let us now look at the differences between soft computing and hard computing.

Parameter |
Soft Computing |
Hard Computing |

Nature | Stochastic | Deterministic |

Functions on | Ambiguous and noisy data | Exact data |

Results | Approximate | Precise |

Logic | Multivalued | Two-valued |

Computations | Parallel | Sequential |

Requires | Robustness | Accuracy |

Nature | Random | Settled |

**What is Soft Computing?**

Soft computing is a group of computational techniques that are based on Artificial Intelligence and natural selection. It provides a cost-effective solution to complex real-life problems for which there is no hard computing solution available. It includes a set of algorithms, including fuzzy logic, evolutionary algorithms, and neural networks. Such algorithms are tolerant of imprecision, partial truth, uncertainty, and approximation.

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**Soft Computing Techniques**

The following are the different soft computing techniques:

### 1. Fuzzy logic

It is a form of many-valued logic in which the true values of variables might be any real number between 0 and 1 rather than just true and false. This mathematical method deals with imprecise or uncertain information. It is used in natural language processing, medical diagnosis, artificial intelligence, control systems, and image processing. The membership function defines the degree of membership of an input value to a certain category or set.

### 2. Neural networks

It is a subset of machine learning that is comprised of node layers consisting of input layer, hidden layers and an output layer. Every node connects to another and has an associated weight and threshold. These rely on training data for learning and improving their accuracy over time. These are used for adaptive control, predictive modeling, and applications that can be trained via datasets. Self-learning happens as a result of experiences within networks. This can derive conclusions from a complex and unrelated set of information.

### 3. Genetic algorithm

A genetic algorithm is a method to solve both constrained and unconstrained optimization problems based on natural selection. This algorithm creates sets of solutions that evolve to get either the lowest or the highest value of an objective function or linear expression. These help in obtaining values that result given a specific objective function.

### 4. Probabilistic reasoning

The concept of probability is used for indicating and identifying the uncertainty of a value. Here, we combine probability theory to handle the uncertainty of value. It is used when an experiment is being conducted, and an unknown error occurs. Probabilistic reasoning is also used when we do not have surety about outcomes and when the predicates are too large to handle.

### 5. Support Vector Machine

It is a type of supervised learning that is used for classification and regression problems. It aims to separate n-dimension space into classes which allows easy identification in which class we can put new data points. However, it has two supporting vectors that create the hyperplane or decision boundary.

**What is Hard Computing?**

It is a conventional approach that is used in computing and needs an accurately stated analytical model. It uses a two-valued logic due to which it has a deterministic nature. The result of hard computing is precise and accurate. Hard computing deals with binary and crisp logic that requires exact input data sequentially. Using mathematical models or algorithms, some of the definite control actions are defined.

Since the real world does not exhibit precise behaviour and the information changes continuously, hard computing does not precisely solve real-world problems. Hard computing is a traditional approach that follows the principles of certainty, precision, and rigor. The input data in the case of hard computing should be exact, which will provide a precise and verifiable output.

### Conclusion

While soft computing does not seek perfect solutions, hard computing results in precision. To overcome this issue, the fusion of soft computing and hard computing is preferred for practical purposes. A number of applications use hard computing and soft computing together to offer economically competitive systems, services, and products.

**About the Author**

Jaya is a writer with an experience of over 5 years in content creation and marketing. Her writing style is versatile since she likes to write as per the requirement of the domain. She has worked on Technology, Fina... Read Full Bio