Modeling and analysis of effects of precipitation and vegetation coverage on runoff and sediment yield in Jinsha River Basin

This paper focuses on the effects of precipitation and vegetation coverage on runoff and sediment yield in the Jinsha River Basin. Results of regression analysis were taken as input variables to investigate the applicability of the adaptive network-based fuzzy inference system （ANFIS） to simulating annual runoff and sediment yield. Correlation analysis indicates that runoff and sediment yield are positively correlated with the precipitation indices, while negatively correlated with the vegetation indices. Furthermore, the results of stepwise regression show that annual precipitation is the most important factor influencing the variation of runoff, followed by forest coverage, and their contributions to the variation ofrunoffare 69.8% and 17.3%, respectively. For sediment yield, rainfall erosivity is the most important factor, followed by forest coverage, and their contributions to the variation of sediment yield are 49.3% and 24.2%, respectively. The ANFIS model is of high precision in runoff forecasting, with a relative error of less than 5%, but of poor precision in sediment yield forecasting, indicating that precipitation and vegetation coverage can explain only part of the variation of sediment yield, and that other impact factors, such as human activities, should be sufficiently considered as well.

Reactive power compensation of an isolated hybrid power system with load interaction using ANFIS tuned STATCOM

This paper presents an adaptive neuro fuzzy interference system （ANFIS） based approach to tune the parameters of the static synchronous compensator （STAT- COM） with frequent disturbances in load model and power input of a wind-diesel based isolated hybrid power system （IHPS）. In literature, proportional integral （PI） based controller constants are optimized for voltage stability in hybrid systems due to the interaction of load disturbances and input power disturbances. These conventional controlling techniques use the integral square error （ISE） criterion with an open loop load model. An ANFIS tuned constants of a STATCOM controller for controlling the reactive power requirement to stabilize the voltage variation is proposed in the paper. Moreover, the interaction between the load and the isolated power system is developed in terms of closed loop load interaction with the system. Furthermore, a comparison of transient responses of IHPS is also presented when the system has only the STATCOM and the static compensation requirement of the induction generator is fulfilled by the fixed capacitor, dynamic compensation requirement, meanwhile, is fulfilled by STATCOM. The model is tested for a 1% step increase in reactive power load demand at t = 0 s and then a sudden change of 3% from the 1% at t = 0.01 s for a 1% step increase in power input at variable wind speed model.

Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

In recent years, many different techniques are applied in order to draw maximum power from photo- voltaic （PV） modules for changing solar irradiance and temperature conditions. Generally, the output power generation of the PV system depends on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilize the collected solar energy optimally. The aim of this paper is to simulate and control a grid-connected PV source by using an adaptive neuro-fuzzy inference system （ANFIS） and genetic algorithm （GA） controller. The data are optimized by GA and then, these optimum values are used in network training. The simulation results indicate that the ANFIS-GA controller can meet the need of load easily with less fluctuation around the maximum power point （MPP） and can increase the convergence speed to achieve the MPP rather than the conventional method. Moreover, to control both line voltage and current, a grid side P/Q controller has been applied. A dynamic modeling, control and simulation study of the PV system is performed with the Matlab/Simulink program.

ANFIS-based Sensor Fusion System of Sit-to-stand for Elderly People Assistive Device Protocols

This paper describes the analysis and design of an assistive device for elderly people under development at the EgyptJapan University of Science and Technology（E-JUST） named E-JUST assistive device（EJAD）.Several experiments were carried out using a motion capture system（VICON） and inertial sensors to identify the human posture during the sit-to-stand motion.The EJAD uses only two inertial measurement units（IMUs） fused through an adaptive neuro-fuzzy inference systems（ANFIS） algorithm to imitate the real motion of the caregiver.The EJAD consists of two main parts,a robot arm and an active walker.The robot arm is a 2-degree-of-freedom（2-DOF） planar manipulator.In addition,a back support with a passive joint is used to support the patient s back.The IMUs on the leg and trunk of the patient are used to compensate for and adapt to the EJAD system motion depending on the obtained patient posture.The ANFIS algorithm is used to train the fuzzy system that converts the IMUs signals to the right posture of the patient.A control scheme is proposed to control the system motion based on practical measurements taken from the experiments.A computer simulation showed a relatively good performance of the EJAD in assisting the patient.

Reactive Navigation of Underwater Mobile Robot Using ANFIS Approach in a Manifold Manner

Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.

Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed coun tries. Intersections with no specific priority to any move ment, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geom etry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neurofuzzy interface system （ANFIS） to the modeling of gap acceptance behavior of rightturning vehicles at limited priority Tintersections （in India, vehicles are driven on the left side of a road）. Field data are collected using video cameras at four Tintersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver＇s decision （accepted/rejected）. ANFIS models are developed by using 80 % of the extracted data （total data observations for major road right turning vehicles are 722 and 1,066 for minor road right turning vehicles） and remaining are used for model vali dation. Four different combinations of input variables are considered for major and minor road right turnings sepa rately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. Themodels developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

Comparative study of various artificial intelligence approaches applied to direct torque control of induction motor drives

In this paper, three intelligent approaches were proposed, applied to direct torque control （DTC） of induction motor drive to replace conventional hysteresis comparators and selection table, namely fuzzy logic, artificial neural network and adaptive neuro-fuzzy inference system （ANFIS）. The simulated results obtained demonstrate the feasibility of the adaptive network-based fuzzy inference system based direct torque control （ANFIS-DTC）. Compared with the classical direct torque control, fuzzy logic based direct torque control （FL-DTC）, and neural networks based direct torque control （NN- DTC）, the proposed ANFIS-based scheme optimizes the electromagnetic torque and stator flux ripples, and incurs much shorter execution times and hence the errors caused by control time delays are minimized. The validity of the proposed methods is confirmed by simulation results.

Navigation of Non-holonomic Mobile Robot Using Neuro-fuzzy Logic with Integrated Safe Boundary Algorithm

In the present work, autonomous mobile robot（AMR） system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neurofuzzy inference system（ANFIS） controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.