Tuning certainty factor and local weight of fuzzy production rules by using fuzzy neural network

Reliability Prediction of Near-Isothermal Rolling of TiAl Alloy Based on Five Neural Network Models

The near-isothermal rolling process has the characteristics of multi-variable and strong coupling, and the industrial conditions change constantly during the actual rolling process. It is difficult to consider the influence of various factors in industrial sites using theoretical derivation, and the compensation coefficient is difficult to accurately determine. The neural network model compensates for the difficulty in determining the compensation coefficient of the theoretical model. The neural network can be trained in advance through historical data, the trained network can be applied to industrial sites for prediction, and previous training errors can be compensated for through online learning using real-time data collected on site. But it requires a large amount of effective historical data, so this research uses a combination of production data from a controllable two-roll rolling mill and finite element simulation to provide training data support for the neural network. Five trained neural networks are used for prediction, and the results are compared with industrial site data, verifying the reliability and accuracy of genetic algorithm optimized neural network prediction. We successfully solved the problem of low control accuracy of TiAl alloy outlet thickness during near-isothermal rolling process.

Fuzzy Petri nets Using Intuitionistic Fuzzy Sets and Ordered Weighted Averaging Operators

Fuzzy Petri nets (FPNs) are an important modeling tool for knowledge representation and reasoning, which have been extensively used in a lot of fields. However, the conventional FPN models have been criticized as having many shortcomings in the literature. Many different models have been suggested to enhance the performance of FPNs, but deficiencies still exist in these models. First, various types of uncertain knowledge information provided by domain experts are very hard to be modeled by the existing FPN models. Second, the traditional FPNs determine the results of knowledge reasoning using the min, max, and product operators, which may not work well in many practical applications. In this paper, we propose a new type of FPN model based on intuitionistic fuzzy sets and ordered weighted averaging operators to deal with the problems and improve the effectiveness of the conventional FPNs. Moreover, a max-algebra-based reasoning algorithm is developed in order to implement the intuitionistic fuzzy reasoning formally and automatically. Finally, a case study concerning fault diagnosis of aircraft generator is presented to demonstrate the proposed intuitionistic FPN model. Numerical experiments show that the new FPN model is feasible and quite effective for knowledge representation and reasoning of intuitionistic fuzzy expert systems.