47 results about "Fuzzy uncertainty" patented technology

The invention relates to an interval value fuzzy set method for aging state evaluation of insulation paper of a transformer, and belongs to the fields of a power system and automation thereof. To solve the problem that big data related to insulation state evaluation of the distribution transformer is diversified, large in amount and complex in mutual relation, knowledge mining and inference principles are used on the basis of establishment of a big data library to process and analyze the big data; and aimed at random and fuzzy nondeterminant parameters related to insulation state evaluation ofthe distribution transformer, an interval value fuzzy set theory is used for processing and analysis, and the aging state of the insulation paper of the transformer is evaluated accurately.

The invention relates to the field of electric power system and automation thereof, in particular to a method for calculating the failure rate of transformer insulation oil deterioration. Aiming at how to deal with the big data problems involving many types, large quantities, and complex interrelationships involved in the assessment of transformer insulation oil degradation fault status, the data clustering principle is used to process and analyze big data on the basis of establishing a large database; for transformer insulation oil degradation The random and fuzzy uncertain parameters involved in fault state evaluation are processed and analyzed by using the theory of probabilistic fuzzy sets. A method for calculating the failure rate of transformer insulation oil degradation proposed by the present invention can evaluate the failure state of transformer insulation oil degradation, which reflects the uncertainty of the characteristic value of the transformer insulation oil degradation fault state, and provides a basis for the evaluation of the transformer insulation oil degradation fault state. Provide theoretical guidance and provide necessary technical support for distribution network operation and maintenance.

The invention discloses a neural network-based micro-gyroscope inversion global sliding mode fuzzy control method, which is based on the neural network inversion global sliding mode fuzzy control system consisting of an inversion global sliding mode controller and a neural network dynamic characteristic estimator and a fuzzy uncertainty estimator. The global sliding mode control can overcome the shortcomings of the arrival mode not being robust in the traditional sliding mode control, speed up the system response, and make the system robust in the whole process of response. The method makes the Lyapunov function of the system and the design process of the controller systematized and structured through reverse design during the inverse control. Using fuzzy control to approximate switching function items, the switching items of sliding mode control are transformed into continuous fuzzy control output, which weakens the chattering phenomenon in sliding mode control and has strong adaptive tracking ability. Therefore, the transient characteristics and robustness of the sliding mode control system are improved, the unknown dynamic characteristics of the micro gyroscope are estimated and chattering in the sliding mode variable structure control is reduced.

The invention discloses a reliability design method of a force applying component of a high-speed press considering multiple types of uncertainties. The method includes the following steps: considering the random, interval and fuzzy uncertainties of high-speed press force components, choosing the minimum reliability value under the influence of three uncertainties as reliability index, establishing the stochastic model of high-speed press force components, and establishing the stochastic model of high-speed press force components; interval-Fuzzy mixed reliability design model; according to theconservation principle of entropy and '3 sigma criterion', a simplified stochastic-interval reliability design model; adopting Latin hypercube sampling and cooperative simulation technology, the Kriging model of function and objective function is constructed. The simplified reliability design model is decoupled from the reliability analysis to form a two-loop optimization solution. The inner loopuses adaptive step-size iterative method for reliability analysis, and the minimum value of reliability index is obtained. The outer loop uses genetic algorithm to optimize the design vector, and judges the feasibility of the design vector according to the reliability analysis results. When the maximum evolutionary algebra or convergence threshold is reached, the optimal solution is output.