New Movement and Transformation Principle of Fuzzy Reasoning and Its Application to Fuzzy Neural Network
This work addresses the need for more efficient and mathematically tractable fuzzy reasoning methods in applications such as data prediction, though it appears incremental as it builds on existing fuzzy systems.
The authors introduced the Movement and Transformation Principle (MTP) for fuzzy reasoning, which modifies consequent fuzzy sets based on operations with antecedent sets and observations, and applied it to fuzzy neural networks. In experiments on precipitation and security data, the method improved learning accuracy and time performance compared to existing approaches like Sugeno and Mamdani systems.
In this paper, we propose a new fuzzy reasoning principle, so called Movement and Transformation Principle(MTP). This Principle is to obtain a new fuzzy reasoning result by Movement and Transformation the consequent fuzzy set in response to the Movement, Transformation, and Movement-Transformation operations between the antecedent fuzzy set and fuzzificated observation information. And then we presented fuzzy modus ponens and fuzzy modus tollens based on MTP. We compare proposed method with Mamdani fuzzy system, Sugeno fuzzy system, Wang distance type fuzzy reasoning method and Hellendoorn functional type method. And then we applied to the learning experiments of the fuzzy neural network based on MTP and compared it with the Sugeno method. Through prediction experiments of fuzzy neural network on the precipitation data and security situation data, learning accuracy and time performance are clearly improved. Consequently we show that our method based on MTP is computationally simple and does not involve nonlinear operations, so it is easy to handle mathematically.