Difference between fuzzy logic and the neural network

Fuzzy logic vs neural network

Fuzzy logic belongs to the family of multivalue logic. It focuses on fixed and approximate reasoning as opposed to fixed and exact reasoning. A variable in fuzzy logic can take a range of truth values ​​between 0 and 1, as opposed to the true or false value of traditional binary sets. Neural networks (NN) or artificial neural networks (ANN) are a computer model developed from biological neural networks. An RNA is made up of artificial neurons that connect to each other. As a general rule, an ANN adapts its structure according to the information that reaches it.

What is fuzzy logic?

Fuzzy logic belongs to the family of multivalue logic. It focuses on fixed and approximate reasoning as opposed to fixed and exact reasoning. A variable in fuzzy logic can take a range of truth values ​​between 0 and 1, as opposed to the true or false value of traditional binary sets. Since the truth value is a range, it can handle a partial truth. The beginning of fuzzy logic was marked in 1956 with the introduction of the theory of fuzzy sets by Lotfi Zadeh. Fuzzy logic provides a method for making final decisions based on imprecise and ambiguous input data. Fuzzy logic is widely used for applications in control systems, because it looks a lot like how a human makes a decision but faster. Fuzzy logic can be incorporated into control systems based on small handheld devices on large PC workstations.

What is a neural network ??

ANN is a computer model developed from biological neural networks. An RNA is made up of artificial neurons that connect to each other. Generally, an ANN adapts its structure according to the information that reaches it. A set of systematic steps called learning rules must be followed when developing an ANN. In addition, the learning process requires learning data to discover the best operating point of the ANN. ANNs can be used to learn an approximation function for certain observed data. But when applying ANN, several factors must be taken into account. The model should be carefully selected based on the data. The use of unnecessarily complex models would make the process of more difficult learning. Choosing the right learning algorithm is also important because some learning algorithms work better with certain types of data.

What is the difference between fuzzy logic and neural networks?

Fuzzy logic makes it possible to make precise decisions based on imprecise or ambiguous data, while ANN attempts to incorporate the human thought process to solve problems without modeling them mathematically. Although these two methods can be used to solve non-linear problems and ill-specified problems, they are not related. Unlike fuzzy logic, ANN attempts to apply the thought process in the human brain to solve problems. In addition, ANN includes a learning process that involves learning algorithms and requires learning data. But there are hybrid intelligent systems developed using these two methods called FNN (Neuro Fuzzy Network) or NFS (Neuro-Fuzzy System).

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