Published the following summer in the journal Information and Control, the paper began with a brief accounting of what Zadeh considered fuzzy: For example, the class of animals clearly includes dogs, horses, birds, etc. as its members and clearly excludes such objects as rocks, fluids, plants, etc.
By comparing results with traditional Monte Carlo simulation and fuzzy reliability model, two numerical examples evidence the feasibility and superiority of proposed method at fuzzy reliability analysis in practical engineering.
The experimental result shows that combining very rough fuzzy prior model with training examples still significantly improves the predictive performance of WEDM process modeling, even with very limited training dataset.
In order to show the applicability of the proposed estimator, indirect adaptive fuzzy control design examples with the proposed estimator are presented.
The properties and examples of fuzzy normed vector spaces, fuzzy algebras, and fuzzy norms are given in [17, 18, 22, 23].
Application of the proposed methodology is demonstrated through illustrative examples, covering fuzzy impulse response of systems, fuzzy Routh–Hurwitz stability criteria, fuzzy controllability and observability.
Process modelling is performed by a learning system, based on a fuzzy learning-by-examples algorithm.
For example, fuzzy axiomatic design approach presents a target-based decision-making supported on common area of membership functions of alternative ratings and target values of attributes.
For example, fuzzy logic can be used together with state space models or neural networks to enhance FDI diagnostic reasoning capabilities.
For example, fuzzy logic has been used in the control of cement manufacture and water purification processes, and a fuzzy controller designed by engineers from Hitachi, Ltd., was used to operate the automatic subway trains of the Japanese city of Sendai.
