Difference Between Fuzzy Logic and Neural Network

Fuzzy Logic vs Neural Network

Fuzzy Logic belongs to the family of many-valued logic. It focuses on fixed and approximate reasoning opposed to fixed and exact reasoning. A variable in fuzzy logic can take a truth value range between 0 and 1, as opposed to taking true or false in traditional binary sets. Neural networks (NN) or artificial neural networks (ANN) is a computational model that is developed based on the biological neural networks. An ANN is made up of artificial neurons that are connecting with each other. Typically, an ANN adapts its structure based on the information coming to it.

What is Fuzzy Logic?

Fuzzy Logic belongs to the family of many-valued logic. It focuses on fixed and approximate reasoning opposed to fixed and exact reasoning. A variable in fuzzy logic can take a truth value range between 0 and 1, as opposed to taking true or false in traditional binary sets. Since the truth value is a range, it can handle partial truth. Beginning of fuzzy logic was marked in 1956, with the introduction of fuzzy set theory by Lotfi Zadeh. Fuzzy logic provides a method to make definite decisions based on imprecise and ambiguous input data. Fuzzy logic is widely used for applications in control systems, since it closely resembles how a human make decision but in faster way. Fuzzy logic can be incorporated in to control systems based on small handheld devices to large PC workstations.

What is Neural Networks?

ANN is a computational model that is developed based on the biological neural networks. An ANN is made up of artificial neurons that are connecting with each other. Typically, an ANN adapts its structure based on the information coming to it. A set of systematic steps called learning rules needs to be followed when developing an ANN. Further, 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 some observed data. But when applying ANN, there are several factors one has to consider. The model has to be carefully selected depending on the data. Using unnecessarily complex models would make the learning process harder. Choosing the correct learning algorithm is also important, since some learning algorithms perform better with certain types of data.

模糊逻辑和神经网络有什么区别？

Fuzzy logic allows making definite decisions based on imprecise or ambiguous data, whereas ANN tries to incorporate human thinking process to solve problems without mathematically modeling them. Even though both of these methods can be used to solve nonlinear problems, and problems that are not properly specified, they are not related. In contrast to Fuzzy logic, ANN tries to apply the thinking process in the human brain to solve problems. Further, ANN includes a learning process that involves learning algorithms and requires training data. But there are hybrid intelligent systems developed using these two methods called Fuzzy Neural Network (FNN) or Neuro-Fuzzy System (NFS).