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Soft Computing vs Hard Computing

Soft Computing vs Hard Computing

Difference between Soft computing vs Hard computing

Soft computing and hard computing are very trending topics when it comes to computing. Soft computing is a paradigm which involves a model that can resolve issues which are not having proper prediction, involves unsure and imprecise solution. On the other hand, hard computing involves a computing paradigm which involves an ancient approach with correct and precise results as part of its workflow. The results are appropriate, exact, and involve a lot of logic that satisfies the model of computing in a proper way. Hard computing model is not considered suitable for real-life predictions.

Head to Head Comparison Between Soft Computing vs Hard Computing (Infographics)

Below are the top 9 differences between Soft Computing vs Hard Computing:

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Comparison Table of Soft Computing vs Hard Computing

Soft Computing Hard Computing
Soft computing involves a computing paradigm that can be judged based on real-life events and thus a computational model can be constructed. Hard computing can never work with real-life events and the model for hard computing cannot get the computational model constructed properly.
Soft computing involves logic with reasoning and needs reason with probabilistic thinking and solution as well. Hard computing on other hand involves binary logic with some of the proper computational models and strategies.
Soft computing has the proper features of approximation and can tolerate ambiguous parameters if in case it arises while performing computation activity. Hard computing involves the computation of features that involves the extraction of specific features and categorical features as well.
Soft computing makes the model stochastic for performing the type of soft computation. Hard computing on other hand makes the computational process deterministic.
Soft computing adjusts well with noisy and ambiguous data. Hard computing adjusts well with the exact type of data as it is a perfect model for exactness.
Soft computing emerges and evolves quite well with its own set of programs. Hard computing emerges and evolves with the need of writing the programs on the console.
It mostly deals with the entire programming paradigm majorly written with approximate results. This type of computation involves the entire programming paradigm to be written with exact and precise results.
Soft computing tries to perform a multivalued logic. Whereas Hard computing tries to perform computation using double-valued logic.
Soft computing involves randomness by taking into consideration random values for evaluation. Hard computing does not involve random values rather it involves values that are fixed and accurate.

Key Differences of Soft Computing vs Hard Computing

Soft computing and hard computing possess comparison but it does possess some of the key differences that need to be highlighted as below:

  • Soft computing never indulges in the computing practices where there is a presence of accurate and proper data rather it is involved in practices where there is the presence of dirty and noisy data, therefore, making the entire data set for computation ambiguous. This means it gels quite well with unprecedented or unpredictable scenarios.
  • Hard computing when compared to soft computing is quite compatible with the perfect and accurate set of data for computation but cannot adjust at all with another set of data which leads to approximation.
  • Soft computing employs some of the logic and probabilistic conditions to measure and get into a conclusion whereas the opposite of it happens when it comes to hard computing because it never works on approximation and probabilistic approach i.e. no logic is used for performing any task on it.
  • The pattern of computation applied for soft computing involves parallel computations whereas computations applied for hard computing is sequential where the data involved can be linear type.
  • Soft computing mostly tries to generate approximate data whereas Hard computing mostly tries to generate data that is accurate and precise.
  • Soft computing paradigm can be used significantly when it comes to solving problems related to real life and helps in making human problems solve with some computational model or prototype.
  • Hard computing is a type of traditional method which is used for making the same and old conventional methods used for prototyping and modeling as well.
  • Soft computing can be applied in various fields like fuzzy logic, artificial intelligence, the genetic algorithm in a proper manner.
  • Hard computing makes the hard computing approach more appropriate for the computational activities which support the same.
  • Genetic algorithm is an evolutionary technique that involves optimization and problems related to creating models for a genetic algorithm that requires heuristic search and metadata search related to automotive design, biomimetic innovation, and many more.
  • Computation problems to be performed in soft computing may include the multivariate type of design and working whereas in hard computing it takes into consideration the double-valued argument for making the statistic and graph in form. It helps in making the activity of deriving the computational model with ease.
  • Even the Artificial intelligence field gels quite well with the Soft computing field as it is totally indulged with prediction and probability-related queries. The form of data present in the analysis is noisy and ambiguous which is what is needed for the Soft computing model to carve out a type of model for prediction.
  • A good example for the artificial intelligence field to work with soft computing is face recognition, or to make the weather or business forecasting patterns using data sets. The models should able to create the proper pattern and should involve soft computing for any work.
  • Since hard computing as mentioned before do not support well with ambiguous and dirty data it is never recommended to make use of these types of data set for computation rather a data set which is binary in nature and has an answer in the form of Boolean values like yes or no should only be involved to make model or prototype. This will help in making the computation process useful for manipulation.

Conclusion

Soft computing when compared to hard computing has a lot of advantages but still, the fact cannot be ignored that when it comes to the computation related to linear type data set then, in that case, it might involve hard computation only. Thus both soft computing and hard computing play a significant role in terms of computation and model fabrication.

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