网络中采用模糊化规则，增强了系统的容错性；（4）模糊神经网络可以同时处

Fuzzy rules are used in the network to enhance the fault tolerance of the system; <br>(4) Fuzzy neural networks can process both known and unascertained information at the same time, expanding the information processing capabilities. <br>Fuzzy neural network is the product of the combination of neural network and fuzzy logic system. It not only has the ability of learning, connection and self-adaptation, but also has the system structure of fuzzy thinking. The structure of the fuzzy inference system is shown in Figure 2.1. The system includes 4 basic units: fuzzy interface, fuzzy inference, fuzzy rule set and defuzzification interface. The fuzzification interface is the mapping from the actual input space to the fuzzy space. The fuzzy rule set is a series of language rules or conditional sentences. Fuzzy reasoning is the core of the fuzzy neural network.

The fuzzy rule is adopted in the network, which enhances the fault tolerance of the system.<br>(4) Fuzzy neural network can process both the accurate and the uncertain information, and expand the information processing capacity.<br>Fuzzy neural network is the product of the combination of neural network and fuzzy logic system, which has the ability of learning, contact and adaptability, but also the system structure of fuzzy thinking. The structure of the fuzzy reasoning system is shown in Figure 2.1, which includes four basic units: fuzzy interface, fuzzy reasoning, fuzzy rule set and fuzzy interface. Fuzzy interface is a mapping from actual input space to fuzzy space, fuzzy rule set is a series of language rules or conditional statements, fuzzy reasoning is the core of fuzzy neural network.

Fuzzy rules are used in the network to enhance the fault tolerance of the system;<br>(4) Fuzzy neural network can deal with both deterministic and unascertained information at the same time, which expands the ability of information processing.<br>Fuzzy neural network is the product of the combination of neural network and fuzzy logic system. It has the ability of learning, connection and self-adaptive, and can carry out fuzzy thinking. The structure of fuzzy inference system is shown in Fig. 2.1. The system consists of four basic units: fuzzy interface, fuzzy reasoning, fuzzy rule set and fuzzy interface. Fuzzification interface is the mapping from actual input space to fuzzy space. Fuzzy rule set is a series of language rules or conditional statements. Fuzzy reasoning is the core of fuzzy neural network<br>