1. A modeling method of neuro-fuzzy system, comprising: a rule-defining process dividing a plurality of training data into a plurality of groups to accordingly define a plurality of fuzzy rules; and a network-building process constructing a fuzzy neural network based on the fuzzy rules obtained by the rule-defining process; wherein the rule-defining process further comprises a sub-process (a) retrieving an input-output pair of the training data, a sub-process (b) determines whether any fuzzy rule is built so as to execute a sub-process (c) if no fuzzy rule is built or to execute a sub-process (e) if at least one fuzzy rule has been built, the sub-process (c) defining a fuzzy rule corresponding to the retrieved input-output pair by function approximation, a sub-process (d) determining whether there is any ungrouped input-output pair of the training data so as to execute the network-building process if no ungrouped input-output pair exists or to execute the sub-process (a) otherwise, and the sub-process (e) calculating an input similarity value and an output difference value between the retrieved input-output pair and a respect fuzzy rule for each one of the fuzzy rules, determining whether the input similarity value of a respect fuzzy rule is larger than or equal to a similarity threshold value as well as the output difference value of this fuzzy rule is smaller than or equal to a difference threshold value, assigning the retrieved input-output pair into a group corresponding to one of the fuzzy rules and then renewing coefficients of this fuzzy rule if any fuzzy rule has an input similarity value not smaller than the similarity threshold value as well as the output difference value thereof is not larger than the difference threshold value or executing the sub-process (c) otherwise, wherein the fuzzy rule corresponding to the group including the retrieved input-output pair has a largest input similarity value.