Definition of Soft Computing Techniques
We use soft computing techniques to solve the complex computational problem, for this soft computing techniques provide us the computing techniques and useful algorithms which can be used to deal with the complex computational problem and complex system. What it does is gives us the partial truth, uncertainty, and tolerance for imprecision for a given particular system or a problem. This type of approach helps us to given and solve problems which are either very much time-consuming by the hardware or are unsolvable, so because of this, it is often termed computational intelligence. In the coming section of the tutorial, we will see how it works, and what are 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.
Soft Computing Techniques
As we discussed by the use of soft computing techniques we are able to solve a complex problems, or in other words, it is more inclined towards the designing and analysis of the 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, are as below;
- Fuzzy logic
- Machine learning
- Artificial neural networks
1) Fuzzy logic:
This is one of the techniques of soft computing, which helps us to get the result from the not clear value. The word ‘fuzzy’ means things that are not pretty much clear or doubtful. In the practice scenario, we have so many situations where we are not able to 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 absolutely true or false, it provides us the flexibility where a value can be partially true or partially false. In short, it provides us the intermediate value to represent this kind of scenario.
This contains four parts, as the component which are mentioned below;
a) Rule Base: By the use of this system we define the rules for the system, it also contains the if-then condition for the system.
b) FUZZIFICATION: With the help of this we can easily convert the input into fuzzy sets.
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c) INFERENCE ENGINE: This component helps us to find the matching value for the current input we passed, and decided which rule needs to be executed based on the input we have passed.
d) DEFUZZIFICATION: it again convert the obtained result into the crips value that is 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 the logic to handle the uncertainty of the value. Like we have so many examples in the real world also like the things will happen or not we are not sure about it.
We can use Probabilistic reasoning in the below three cases provided;
a) When we are trying to do an experiment and something happened like an unknown error.
b) When we are not sure about uncertain outcomes.
c) 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 which requires some prior instruction, they are designed to solve a various type of problem such as nonlinear complex problem, it is mostly inspired or we can say ideal for the 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 details;
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 with each layer using the output of the previous layer 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.
- The term “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 and it processes those inputs to multiple neurons which again passes through HIDDEN LAYER and produces the output layer.
- Backward propagation: Now we compare our result with the actual output. But while doing the forward propagation we can have some error, so we try to minimize the value/weight of that neuron those are contributing more to the error. For this, we need to backtrack the process. To match the desired output.
The below diagram is showing how it works. Input can be anything like an Image to extract the feature and compare those with other images and so on.
Flow chart:
Input Layer Hidden Layer Output Layer
Conclusion
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 like difficult to solve.
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