Nonlinear Mapping Model Research Articles

An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT.

Electrical capacitance tomography (ECT) is a promising imaging technology of permittivity distributions in multiphase flow. To reduce the effect of charging phenomenon on ECT measurement, an improved extreme learning machine method combined with adaptive soft-thresholding (AST-ELM) is presented and studied for image reconstruction. This method can provide a nonlinear mapping model between the capacitance values and medium distributions by using machine learning but not an electromagnetic-sensitive mechanism. Both simulation and experimental tests are carried out to validate the performance of the presented method, and reconstructed images are evaluated by relative error and correlation coefficient. The results have illustrated that the image reconstruction accuracy by the proposed AST-ELM method has greatly improved than that by the conventional methods under the condition with charging object.

Dividing traffic cluster into parts by signal control

When a cluster of vehicles with various speeds moves through the series of signals, the cluster breaks down by stopping at signals and results in smaller groups of vehicles. We present the nonlinear-map model of the motion of vehicles controlled by the signals. We study the breakup of a cluster of vehicles through the series of signals. The cluster of vehicles is divided into various groups by controlling the cycle time of signals. The vehicles within each group move with the same mean velocity. The breakup of the traffic cluster depends highly on the signal control. The dependence of dividing on both cycle time and vehicular speed is clarified. Also, we investigate the effect of the irregular interval between signals on dividing.

Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model

In this study, we propose a non-linear random mapping model called GELM. The proposed model is based on a combination of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the Extreme Learning Machine (ELM), and can be used to calculate Value-at-Risk (VaR). Alternatively, the GELM model is a non-parametric GARCH-type model. Compared with conventional models, such as the GARCH models, ELM, and Support Vector Machine (SVM), the computational results confirm that the GELM model performs better in volatility forecasting and VaR calculation in terms of efficiency and accuracy. Thus, the GELM model can be an essential tool for risk management and stress testing.

Effect of periodic inflow on speed-controlled shuttle bus

Abstract We investigate the dynamic behavior of a shuttle bus controlled the speed when passengers come periodically at the origin. We propose the nonlinear-map model for the dynamics of the speed-controlled bus with the periodic inflow. The bus schedule is closely connected to the motion. The motion of the speed-controlled bus is affected by the periodic inflow. The motion of the shuttle bus depends highly on both speed control and periodic inflow. The shuttle bus displays the periodic, quasi-periodic, and chaotic motions by varying both periodic inflow and speed control. We clarify the dependence of the bus motion on both speed control and periodic inflow.

A Novel Enhancement Algorithm Combined with Improved Fuzzy Set Theory for Low Illumination Images

A novel enhancement method of global brightness modulation and local contrast enhancement combined with the improved fuzzy set theory is proposed for color image contrast enhancement. The proposed method consists of three stages. Firstly, putting forward nonlinear global brightness mapping model adjusts dynamic range of images for luminance componentVofHSVcolor space. Secondly, membership function is established in stages to adjust local contrast of image details nonlinearly based on fuzzy set theory. Finally, the enhanced images are transformed fromHSVcolor space intoRGBcolor space. The experiments further show that the proposed method has the shortest processing time, the highest AIC values, and the least NIQE values among the other four conventional methods. It has excellent effect, which can enhance the global brightness and local contrast, and advance visibility of low illumination images.

Financial Classification of Listed Companies in China Based on BP Neural Network Method

Based on analyzing and studying back propagation (BP) neural network method, the article takes 38 cross-section data as modeling sample, and uses 18 data at the same time as the examination sample to establish the financial distinction model. Passed through training and studies repeatedly to the sample, we obtained the more precise forecast result. The findings indicated: the BP neural network is one kind of non-linear mapping model. In the situation that the degree of correlation among the indicators is high, or the data present nonlinearities change, or the data have omissions and so on, using BP neural network may obtain the quite satisfactory result, therefore it is a quite ideal forecast method, and has the widespread application scope and the high reference value.

Analysis and control of chaotic behavior in boost converter by ramp compensation based on Lyapunov exponents assignment: theoretical and experimental investigation

Nonlinear analysis of complex dynamics displayed by current mode dc–dc converter and idea of Lyapunov exponents assignment by ramp compensator in order to control chaotic behavior is proposed in this article. A discrete-time iterative nonlinear mapping model is derived. The occurrence of the complex behaviors of bifurcation and chaos generated by varying the circuit parameters are investigated through numerical analysis and software implementation of the circuit. Next, in order to control bifurcation and chaos in these converters, the ramp compensation method is used. By inserting the ramp compensation parameter in the dynamical equations of the system, these complex behaviors are examined theoretically and numerically as well. It is proved that through this method, the stable period-one operation of the converter can be extended. By evaluating the Lyapunov exponents (LEs) of the system, the impact of the slope on the location of LEs are determined analytically. This leads to a design methodology for control of chaos in this converter based on LEs assignment in desired values by proper selection of compensator slope. By developing an experimental set up, practical results are obtained to confirm the theoretical analysis and simulations.

An extreme learning machine combined with Landweber iteration algorithm for the inverse problem of electrical capacitance tomography

The image reconstruction of the electrical capacitance tomography (ECT) is an ill-posed and sparse problem. In order to increase the accuracy and speed of the image reconstruction, this paper proposes a new reconstruction algorithm which is based on the extreme learning machine (ELM) with the Landweber iteration method. Firstly, a nonlinear mapping model is established between the pixel gray-scale values and the interelectrode capacitances by using the ELM which has a good learning ability and high speed. Secondly, the Landweber iteration method, which has a good performance in convergence and stability, is applied to calculate the output weight matrix of ELM. Finally, a convergence and stable mapping model of ELM with the Landweber iteration algorithm (L-ELM) for ECT image reconstruction is trained on Matlab platform. Both simulation and measurement tests are carried out to evaluate and analyze the proposed method. Experimental results indicate that the proposed algorithm has good generalization ability and high image reconstruction quality which are better than those of conventional ELM algorithm.

A new open-loop fiber optic gyro error compensation method based on angular velocity error modeling.

With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage and temperature as the input variables and angular velocity error as the output variable. Firstly, the angular velocity error is extracted from OFOG output signals, and then the output voltage , temperature and angular velocity error are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, and is established and thus can be calculated automatically to compensate OFOG errors according to and . The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by , and relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by , and , respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity.

Driving behavior and control in traffic system with two kinds of signals

We study the vehicular traffic controlled by two kinds of signals which are positioned with a periodic configuration. We propose a microscopic model to explore the driving behavior in the traffic system with two kinds of signals. The control method of traffic flow by the combination of two kinds of signals is proposed. The dynamic model is described by the nonlinear map model and the CA model. The driving behavior is clarified for the traffic system controlled by two kinds of signals. The fundamental diagrams are derived for various combinations of two kinds of signals. The traffic flow through two kinds of signals is compared with that of a single kind of signals. The traffic flow displays the complex behavior different from the conventional traffic with a single kind of signals.

Nonlinear-map model for the control of an airplane schedule

We study the dynamics and control of an airplane in air transportation between two airports. The dynamic models are presented for the airplane schedule. The dynamics of an airplane is described by the piecewise map and delayed map models. The characteristics of the nonlinear maps are studied analytically and numerically. The airplane displays the complex motion in the unstable region. The motion of the airplane depends on the quantity of goods, the control method, and the delay. It is shown that the control delay has an important effect on the motion.

DIRECT TORQUE CONTROL USING NEURAL NETWORK APPROACH

Direct Torque Control (DTC) is one of the latest technique to control the speed of motor, in this paper, the control technique of DTC is based on when load changes then inverter switch position are changed and supply to the motor is changed, in this paper Proportional Integral (PI), Neural Network (NN) controller and Adaptive motor model is designed this is the heart of the DTC, as we know that DTC doesn’t require any feedback and sensors to measure. The NN structure is to be implemented by input output (nonlinear) mapping models and is constructed with input, output and hidden layers of sigmoid activation functions. It has been introduced as a possible solution to the real multivariate interpolation problem. To improve the performance of DTC with the modern technique using NN approach is implemented, and performance of DTC with PI controller and NN controller is done, hence, the NN approach shows the better performance than conventional PI controller.

Image reconstruction for an Electrical Capacitance Tomography (ECT) system based on a least squares support vector machine and bacterial colony chemotaxis algorithm

Electrical capacitance tomography (ECT) is a computational imaging technology that may be applied to visualize and quantify the cross-sectional permittivity distribution of gas/solid two-phase flow. In consideration of the nonlinearity and ill-posed characteristics of image reconstruction in ECT, this paper presents a new image reconstruction method based on the least squares support vector machine (LSSVM) combined with the bacterial colony chemotaxis (BCC) algorithm to meet the requirements in the observation of transient flow regimes and their evolution from the spout of a dense–dilute burner at the end of a pneumatic conveying pulverized coal system. Firstly, a nonlinear mapping model is established from the measured capacitances to the grayscale values of the image by using the LSSVM, which has good nonlinear learning ability and high convergence rate. Secondly, because it is difficult to select kernel parameters for the LSSVM model, the BCC algorithm, which has global optimization and rapid convergent ability, is applied to construct an objective optimization function for the kernel parameters. Finally, a pneumatic conveying system with a radial biased whirl burner (a typical dense–dilute burner) was built up, and a sequence of images was collected using a 12-electrode ECT system from the spout of the burner to verify the effectiveness of the reconstruction algorithm under several cold conditions. Experimental results indicate that the algorithm can achieve reconstructed images with good quality and identify the subtle change of the transitional flow regimes from the spout of the burner.

Optimization on Process Parameters of Brewing <i>Ganoderma lucidum</i> Functional Drinking Vinegar

Through the biological reaction caused by self-detached acetic acid bacteria in the ganoderma lucidum deep fermented liquid, the process parameters of producing ganoderma lucidum functional drinking vinegar have been researched. Using the orthogonal experiment and the method of neural network, the nonlinear mapping model among the ganoderma lucidum functional drinking vinegar's process parameters has been established. Through the optimization, the best process parameters were: ethanol quantity was 6.7±0.2%, ganoderma lucidum liquid quantity was 32±1%, reaction time was 74h, inoculum concentration of acetic acid bacteria was 10%.

Nonlinear-map model for bus schedule in capacity-controlled transportation

We study the schedule of shuttle buses in the transportation system controlled by capacity. The bus schedule is closely related to the dynamic motion of buses. We present the nonlinear-map model for the dynamics of shuttle buses. The motion of shuttle buses depends on the inflow rate. The dependence of the fixed points on the inflow is derived. The dynamic transitions occur with increasing the value of inflow rate. At the dynamic transition point, the motion of buses changes from a stable state to an unstable state and vice versa. The shuttle buses display periodic, quasi-periodic, and chaotic motions in the unstable state. In the unstable state, the number of riding passengers fluctuates complexly with varying trips. The bus schedule is governed by the complex motion of buses.

Feature extraction and dimensionality reduction of arc sound under typical penetration status in metal inert gas welding

Arc sound is well known as the potential and available resource for monitoring and controlling of the weld penetration status, which is very important to the welding process quality control, so any attentions have been paid to the relationships between the arc sound and welding parameters. Some non-linear mapping models correlating the arc sound to welding parameters have been established with the help of neural networks. However, the research of utilizing arc sound to monitor and diagnose welding process is still in its infancy. A self-made real-time sensing system is applied to make a study of arc sound under typical penetration status, including partial penetration, unstable penetration, full penetration and excessive penetration, in metal inert-gas (MIG) flat tailored welding with spray transfer. Arc sound is pretreated by using wavelet de-noising and short-time windowing technologies, and its characteristics, characterizing weld penetration status, of time-domain, frequency-domain, cepstrum-domain and geometric-domain are extracted. Subsequently, high-dimensional eigenvector is constructed and feature-level parameters are successfully fused utilizing the concept of primary principal component analysis (PCA). Ultimately, 60-demensional eigenvector is replaced by the synthesis of 8-demensional vector, which achieves compression for feature space and provides technical supports for pattern classification of typical penetration status with the help of arc sound in MIG welding in the future.

Complex motion of shuttle buses in a transportation system reducing energy consumption

We study the dynamical behavior and transitions of shuttle buses in a transportation system reducing energy consumption. We present the nonlinear-map model for the dynamics of M buses. The motion of shuttle buses depends on the loading parameter and the number. The dependence of the fixed points on the loading parameter is derived. The dynamical transitions occur at 2(M−1) stages with increasing the value of loading parameter. At the dynamical transition point, the motion of buses changes from a stable (an unstable) state to an unstable (a stable) state. The shuttle buses display periodic motions with various periods in the unstable state. In the unstable state, the number of riding passengers fluctuates complexly with varying trips.

Complex motion in nonlinear-map model of elevators in energy-saving traffic

We have studied the dynamic behavior and dynamic transitions of elevators in a system for reducing energy consumption. We present a nonlinear-map model for the dynamics of M elevators. The motion of elevators depends on the loading parameter and their number M. The dependence of the fixed points on the loading parameter is derived. The dynamic transitions occur at 2 ( M − 1 ) stages with increasing the value of loading parameter. At the dynamic transition point, the motion of elevators changes from a stable state to an unstable state and vice versa. The elevators display periodic motions with various periods in the unstable state. In the unstable state, the number of riding passengers fluctuates in a complex manner over various trips.

Vehicular motion in 2D city traffic network with signals controlled by phase shift

We study the dynamic behavior of vehicular traffic through the series of traffic lights controlled by phase shift in two-dimensional (2D) city traffic network. The nonlinear-map model is presented for the vehicular traffic. The city traffic network is made of one-way perpendicular streets arranged in a square lattice with traffic signals where vertical streets are oriented upwards and horizontal streets are oriented rightwards. There are two traffic lights for the movement to north or that to east at each crossing. The traffic lights are controlled by the cycle time, split, and phase shift. The vehicle moves through the series of signals on a path selected by the driver. The city traffic with a heterogeneous density distribution is also studied. The dependence of the arrival time on cycle time, split, phase shift, selected path, and density is clarified for 2D city traffic. It is shown that the vehicular traffic is efficiently controlled by the phase shift.

Camera calibration Using Receptive fields

Camera calibration is to identify a model that infers 3-D space measurements from 2-D image observations. In this paper, the nonlinear mapping model of the camera is approximated by a series of linear input–output models defined on a set of local regions called receptive fields. Camera calibration is thus a learning procedure to evolve the size and shape of every receptive field as well as parameters of the associated linear model. Since the learning procedure can also provide an approximation extent measurement for the linear model on each of the receptive fields, calibration model is consequently obtained from a fusion framework integrated with all linear models weighted by their corresponding approximation measurements. Since each camera model is composed of several receptive fields, it is feasible to unitedly calibrate multiple cameras simultaneously. The 3-D measurements of a multicamera vision system are produced from a weighted regression fusion on all receptive fields of cameras. Thanks to the fusion strategy, the resultant calibration model of a multi-camera system is expected to have higher accuracy than either of them. Moreover, the calibration model is very efficient to be updated whenever one or more cameras in the multi-camera vision system need to be activated or deactivated to adapt to variable sensing requirements at different stages of task fulfillment. We studied the Simulation proposed by Jianbo Su in this paper and we are trying to implement his proposed model. Sachin Agrawal Student Prof. Ram Meghe Institute of Tech & Research Badnera S.A.Bhura Student Prof. Ram Meghe Institute of Tech & Research Badnera The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org