The need for soft computing arises from the limitations of traditional, classical computing methods in solving real-world problems. 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:
- Complexity of real-world problems: Many real-world problems are complex and involve uncertainty, vagueness, and imprecision. Traditional computing methods are not well-suited to handle these complexities.
- Incomplete information: In many cases, there is a lack of complete and accurate information available to solve a problem. Soft computing techniques can provide approximate solutions even in the absence of complete information.
- Noise and uncertainty: Real-world data is often noisy and uncertain, and classical methods can produce incorrect results when dealing with such data. Soft computing techniques are designed to handle uncertainty and imprecision.
- Non-linear problems: Many real-world problems are non-linear, and classical methods are not well-suited to solve them. Soft computing techniques such as fuzzy logic and neural networks can handle non-linear problems effectively.
- Human-like reasoning: Soft computing techniques are designed to mimic human-like reasoning, which is often more effective in solving complex problems.
Overall, soft computing provides an effective and efficient way to solve complex real-world problems that are difficult or impossible to solve using classical computing methods.
In this article, we will cover the need for soft computing and why it is important. So, to understand the need for soft computing let us first understand the concept of computing.
Concept of computing :
According to the concept of computing, the input is called an antecedent and the output is called the consequent. For example, Adding information in DataBase, Compute the sum of two numbers using a C program, etc.
There are two types of computing as following :
Characteristics of hard computing :
- The precise result is guaranteed.
- The control action is unambiguous.
- The control action is formally defined (i.e. with a mathematical model)
Now, the question arises that if we have hard computing then why do we require the need for soft computing.
Characteristics of soft computing :
- It may not yield a precise solution.
- Algorithms are adaptive.
- In soft computing, you can consider an example where you can see the evolution changes for a specific species like the human nervous system and behavior of an Ant’s, etc.
- Learning from experimental data.
Need For Soft Computing :
- Many analytical models are valid for ideal cases. Real-world problems exist in a non-ideal environment.
- Soft computing provides insights into real-world problems and is just not limited to theory.
- Hard computing is best suited for solving mathematical problems which give some precise answers.
- Some important fields like Biology, Medicine and humanities, etc are still intractable using Convention mathematical and Analytical models.
- It is possible to map the human mind with the help of Soft computing but it is not possible with Convention mathematical and Analytical models.
Examples –
Consider a problem where a string w1 is “abc” and string w2 is “abd”.
- Problem-1 :
Tell that whether w1 is the same as w2 or not?Solution –
The answer is simply No, it means there is an algorithm by which we can analyze it. - Problem-2 :
Tell how much these two strings are similar?Solution –
The answer from conventional computing is either YES or NO. But these maybe 80% similar, this can be answered only by Soft Computing.
Recent development in Soft Computing :
- In the field of Big Data, soft computing working for data analyzing models, data behavior models, data decision, etc.
- In case of Recommender system, soft computing plays an important role for analyzing the problem on the based of algorithm and works for precise results.
- In Behavior and decision science, soft computing used in this for analyzing the behavior, and model of soft computing works accordingly.
- In the fields of Mechanical Engineering, soft computing is a role model for computing problems such that how a machine will works and how it will make the decision for a specific problem or input given.
- In this field of Computer Engineering, you can say it is core part of soft computing and computing working on advanced level like Machine learning, Artificial intelligence, etc.
Advantages of Soft Computing:
- Robustness: Soft computing techniques are robust and can handle uncertainty, imprecision, and noise in data, making them ideal for solving real-world problems.
- Approximate solutions: Soft computing techniques can provide approximate solutions to complex problems that are difficult or impossible to solve exactly.
- Non-linear problems: Soft computing techniques such as fuzzy logic and neural networks can handle non-linear problems effectively.
- Human-like reasoning: Soft computing techniques are designed to mimic human-like reasoning, which is often more effective in solving complex problems.
- Real-time applications: Soft computing techniques can provide real-time solutions to complex problems, making them ideal for use in real-time applications.
Disadvantages of Soft Computing:
- Approximate solutions: Soft computing techniques provide approximate solutions, which may not always be accurate.
- Computationally intensive: Soft computing techniques can be computationally intensive, making them unsuitable for use in some real-time applications.
- Lack of transparency: Soft computing techniques can sometimes lack transparency, making it difficult to understand how the solution was arrived at.
- Difficulty in validation: The approximation techniques used in soft computing can sometimes make it difficult to validate the results, leading to a lack of confidence in the solution.
- Complexity: Soft computing techniques can be complex and difficult to understand, making it difficult to implement them effectively.
