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To mimic the human steering manoeuvre in curve driving, a human-like driver model with visual perception and decision-making modules was proposed. Inspired by the studies of human visual behavior in curve driving, the visual perception module was established to extract the visual inputs of vehicle speed, lateral deviation at near zone and heading angle error at far zone for steering decision from road information and vehicle motion states. Because of the fuzzy reasoning and complex characteristic of human driving behavior, the driving simulator experiments were conducted on multi-curvature curved roads at different speeds to acquire the training data, and an adaptive neuro-fuzzy inference system(ANFIS) was adopted to establish the decision-making module of steering wheel angle. Based on PreScan /Simulink joint simulation, the human-like driver model was verified at different speeds on different curved roads. The results show that the model has basic road following ability, and the generated angle changes smoothly, which can reflect the general human steering behavior in curve driving with a certain applicability to other driving conditions. [ABSTRACT FROM AUTHOR]

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Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem of weapon system portfolio decision-making, this paper proposed a new portfolio decision model based on adaptive fuzzy neural inference. First, it described combat effectiveness and agility of the system by fuzzy sets represented by Gaussian functions. Then, it classified the weapon systems and the adaptive fuzzy neural model established based on Boston's portfolio matrix. Finally, it optimized the parameters which were related to the model by Satin bowerbird algorithm. The simulation result on the sample database shows that the method can reflect the state of the system of systems, so that the decision maker can adjust and update the portfolio scheme according to the demand. In addition, the optimized model based on Satin bowerbird algorithm can improve the classification accuracy to some extent, and has lower mean square error and higher error tolerance than the traditional model. [ABSTRACT FROM AUTHOR]

In order to solve the fault recognition problems caused by a complex signal transfer path,severe noise pollution and the weak fault features,a method for gear based on variational mode decomposition（ VMD）and adaptive neuro-fuzzy inference system（ ANFIS） is proposed. Firstly,VMD is used to decompose a fault signal into several intrinsic mode functions（ IMFs）,and introduced the permutation entropy to construct the feature vectors characterizing the modal component information. Then the extracted feature vectors are input into the adaptive neuro-fuzzy inference system to establish the fault diagnosis model. Finally,the model is validated by the vibration signal data of the gears and compared with the support vector machine（ SVM） method. The results show that the proposed method has a strong learning ability and it can effectively diagnose the gear fault,improve the accuracy of fault identification. The recognition effect is obviously superior to SVM.

Aiming at the precise control of the common-rail pressure of the high pressure common-rail diesel engine, the control method of the common-rail pressure was summarized, and a common-rail pressure control algorithm based on the adaptive neuro-fuzzy inference system (ANFIS) and PID controller was proposed. Firstly, in the Matlab/Simulink environment, the simulation model of the control algorithm of rail pressure was established by using the adaptive neural fuzzy PID controller, and the simulation was compared with the conventional PID control. Secondly, the fluctuation curve of the common-rail pressure was monitored by experiment, the control effect of the two control methods under the two kinds of transition conditions of starting and accelerating was observed. The results indicate that the steady state, dynamic characteristics and the adaptive neural fuzzy PID control are obviously better than those of conventional PID control. At the same time, under the two transition conditions of starting and accelerating, the fluctuation range of common-rail pressure is small, meeting the requirement of common-rail pressure stability in practical application. [ABSTRACT FROM AUTHOR]

An adaptive neuro-fuzzy inference system (ANFIS) based predictor-corrector guidance is proposed for the nonlinearity of the reentry kinematic equations of hypersonic vehicles. The energy-based three degrees of freedom reentry kinematic equations are established, and the longitudinal and lateral guidance laws are designed. For the longitudinal guidance, an ANFIS controller is designed by taking energy and range error as system inputs and bank angle variation as system output. The lateral guidance is based on the approximate linearity between crossrange and energy. A lateral reversal logic based on the cross range corridor is designed. Simulation results show that the proposed guidance law has the advantages of less calculating time and High guidance precision, and is not sensitive to the initial errors and disturbances. [ABSTRACT FROM AUTHOR]

Bearingless induction motors, which were multivariable, were strongly coupled, along with a higher order nonlinear system. To obtain the stable suspension control of a bearingless induction motor, a new control strategy based on Adaptive Neuro Fuzzy Inference System was proposed. First, in the analysis of the generation mechanism of a bearingless induction motor's radial suspension force, the mathematical model of a bearingless induction motor was achieved. Based on the control principle of an Adaptive Neuro Fuzzy Inference System, the Adaptive Neuro Fuzzy Inference System had been built to design the controller, including the option of control variables and membership functions. By the PID control, the input data and output data could be collected. The selected criterion of error was set to correct the membership function parameters. In addition, the Fuzzy Inference System (FIS) model was trained by a Sugeno type Adaptive Neuro Fuzzy Inference System controller. Then, aiming at the performances of rotor suspending, speed, and torque response, the simulation and analysis of the control system for bearingless induction motors had been carried out on the basis of MATLAB/Simulink simulation platform. Moreover, the motor speed was set to 6000r/min. The simulation results showed that the stable suspension of a bearingless induction motor can be quickly achieved by this presented control strategy. Through the comparison with PID control, the speed response was faster, and the speed overshoot was smaller in the Adaptive Neuro Fuzzy Inference System control. Further, the suspension performance of the rotor was not affected by the sudden change in the load torque. When the rotor speed suddenly changed from 6000r/min to 3000r/min at the time of 0.5 seconds, the speed response of the control system could track the given speed well, and with a very small steady state error. The control system has a fine dynamic and static performance. Finally, the control system test platform of a bearingless induction motor was built based on Adaptive Neuro Fuzzy Inference System controller. The experimental results of the control system also showed that this control strategy could achieve the stable suspension of a bearingless induction motor. The control system has a quickly response, a high control precision, and the strong robustness to load torque disturbance. The correctness and effectiveness of the Adaptive Neuro Fuzzy Inference System control method was verified in this paper. [ABSTRACT FROM AUTHOR]