Updated June 12, 2023
Definition of Soft Computing Techniques
We use soft computing techniques to solve complex computational problems, and these soft computing techniques provide us with computing techniques and useful algorithms which can be used to deal with complex computational problems and complex systems. What it does is gives us partial truth, uncertainty, and tolerance for imprecision for a given particular system or a problem. This approach helps us to give and solve problems that are either very much time-consuming by the hardware or are unsolvable, so it is often termed computational intelligence. In the coming section of the tutorial, we will see how it works and the different scenarios where we can use and implement this for better uses and performance of our system and to solve the complex problem in detail.
Different Soft Computing Techniques
As we discussed, by using soft computing techniques, we can solve complex problems, or in other words, it is more inclined towards designing and analyzing intelligence systems. Soft computing techniques such as fuzzy logic, neural networks, and many more help us to get the solution for a complex problem.
In this section, we will discuss the different soft computing techniques which are available to solve the complex system problem, as below;
- Fuzzy logic
- Machine learning
- Artificial neural networks
1. Fuzzy Logic
This is one of the soft computing techniques which helps us get the result from the unclear value. The word ‘fuzzy’ means things that are unclear or doubtful. In the practice scenario, we have so many situations where we cannot determine the value for a state, like whether it is true or false, based on some logic. So this fuzzy logic provides us the flexibility by which we can overcome such situations and can able to pass the uncertainties and inaccuracies of the situation in a better way. If we talk about the system value, then we have 1, which is responsible for the true value, and on another side, we have 0, which represents the absolute false value. But in the case of fuzzy logic, we do not have such scenarios where the value is true or false; it provides us the flexibility where a value can be partially true or partially false. In short, it gives us the intermediate value to represent this scenario.
This contains four parts, as the component which are mentioned below;
- Rule Base: By using this system, we define the rules for the system; it also contains the if-then condition for the system.
- FUZZIFICATION: With the help of this, we can easily convert the input into fuzzy sets.
- INFERENCE ENGINE: This component helps us to find the matching value for the current input we passed and decide which rule needs to be executed based on the input we have passed.
- DEFUZZIFICATION: It again converts the obtained result into the crips value, the input value.
2. Probabilistic Reasoning
In this technique, we used the probability or the concept of probability, which will help us to indicate and identify the uncertainty of the value. In this approach, what we do, we combine the probability theory or concept with logic to handle the uncertainty of the value. As we have so many examples in the real world, things will happen or not. We are not sure about it.
We can use Probabilistic reasoning in the below three cases provided;
- Something happens when we are trying to experiment, like an unknown error.
- When we are not sure about uncertain outcomes.
- When we have predicates too large to handle.
Below we have the formula to calculate it:
We can find the probability of an event by using the below formula –
probability of occurrence = no. of desired outcomes/ total no of outcomes
3. Artificial Neural Networks
Artificial Networks helps us to solve a problem that requires some prior instruction; they are designed to solve various type of problem, such as nonlinear complex problem, it is mainly inspired by, or we can say, ideal for biological neural networks, whose performance can be compared with most of the human brain functions.
Below we explain the working of the neural networks in detail:
1. Input Layer
2. Hidden Layer
3. Output Layer
4. They can have more than one hidden layer.
5. The layers are interconnected vis nodes or neurons, each using the previous layer’s output as its input.
6. Its main function is to take a set of inputs, perform calculations and then use the output to solve the problem.
- “DEEP” usually refers to the number of hidden layers in the neural network. A traditional neural network contains 2-3 hidden layers, but a deep neural network can have as many as 150 layers.
- Forward Propagation: In this, we pass several inputs to the input layer, which processes those inputs to multiple neurons, which again pass through HIDDEN LAYER and produce the output layer.
- Backward propagation: Now, we compare our result with the actual output. But while doing the forward propagation, we can have some errors, so we try to minimize the value/weight of that neuron that contributes more to the error. For this, we need to backtrack the process. To match the desired output.
The below diagram shows how it works. Input can be anything like an Image to extract and compare the features with other images.
Input Layer Hidden Layer Output Layer
As of now, we have already seen some of the soft computing techniques which explain how we can provide the inputs and based on which we can get the result; we can use these techniques to get the desired result and value for the complex problem which seems difficult to solve.
This is a guide to Soft Computing Techniques. Here we discuss the definition and different soft computing techniques, respectively. You may also have a look at the following articles to learn more –