The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A metal-free arylsulfonyl radical-triggered desulfonylation of N-aryl-N-arylsulfonyl-acrylamides under mild conditions for the facile synthesis of a series of sulfonylated amides has been described. The radical transformation simultaneously installs C–S and C–C bonds with concomitant cleavage of N–S and C–S bonds through continuous 5-exo-trig cyclization, desulfonylation, and aryl migration sequence.
In order to solve the fault diagnosis problem of vibration Parameter, this dissertation proposes the application of adaptive neural network-based fuzzy inference system to engine error diagnosis. Different from the fuzzy inference system, the membership function adopted in this method is no longer a fixed entity but an optimal one achieved by the practice of neural network, which adopts the method...
Adaptive neural network-based fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine, the thesis, with the construction of ANFIS, by using gradient descent genetic algorithm and optimization of system parameters of neutral network learning algorithm, inputs the fusion data into ANFIS, the ANFIS fault diagnosis model adopts the method of information fusion...
This paper, in order to reduce fault and improve ratio of recognition, build adaptive neural network-based fuzzy inference system (ANFIS), which was applied to build a fault diagnosis model of automobile engine, adopts the method of information fusion in entropy method to optimize the input interface. To reduce the impact of excessive parameters on classification accuracy and cost, it also raises...
In order to solve the fault diagnosis problem of Vibration Parameter, Adaptive Neuro-Fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine and induce cloud model of fan-out, outputting results are continued. Through verification of the built diagnosis model with data of engine tests, it has been found that the recognition accuracy increase from 88.75% to...
The thesis introduces grey system model and BP neural network. Through making full use of the merits of GM(1.1) and neural network model and overcoming their drawbacks, we construct the grey residue amending combined and prediction model based on BP Neural network, and such combined model as "combined prediction model= tendency prediction model/GM(1.1)+neural network model", and makes a...
The thesis introduces grey system model and BP neural network. Through making full use of the merits of GM(1.1) and neural network model and overcoming their drawbacks, we construct the grey residue amending combined and prediction model based on BP Neural network, and such combined model as “combined prediction model= tendency prediction model/GM(1.1)+neural network model”, and makes a contrast between...
In order to solve the fault diagnosis problem of performance Parameter, Adaptive Neuro-Fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine and induce cloud model of fan-out, outputting results are continued. Through verification of the built diagnosis model with data of engine tests, it has been found that the recognition accuracy increase from 84.38% to...
This paper builds the fault diagnosis model and optimizes the input interface of the model by normalizing the initial data of the performance parameter for the automobile engine, carrying on information fusion and adopting the Adaptive Neural Fuzzy Interference System (ANFIS). The recognition rate of the model reaches 94.38% under the test of field test data. Corresponding BP neural network modeling...
This paper builds the fault diagnosis model and optimizes the input interface of the model by normalizing the initial data of the Oil parameter for the automobile engine, carrying on information fusion and adopting the Adaptive Neural Fuzzy Interference System (ANFIS). The recognition rate of the model reaches 90.26% under the test of field test data. The experiment indicates that the model enjoys...
The paper builds the fault diagnosis model and optimizes the input interface of the model by normalizing the initial data of the vibration parameter for the automobile engine, carrying on information fusion and adopting the Adaptive Neural Fuzzy Interference System (ANFIS). The recognition rate of the model reaches 91.25% under the test of field test data. The experiment indicates that the model enjoys...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.