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The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm.

Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives.

Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data.

The proposed method requires iterative calculation and can only identify parameters offline.

The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems.

In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

Weather forecasting is the trending topic around the world as it is the way to predict the threats posed by extreme rainfall conditions that lead to damage the human life and properties. These issues can be managed only when the occurrence of the worse weather is predicted in advance, and sufficient warnings can be executed in time. Thus, keeping in mind the importance of the rainfall prediction system, the purpose of this paper is to propose an effective rainfall prediction model using the nonlinear auto-regressive with external input (NARX) model.

The paper proposes a rainfall prediction model using the time-series prediction that is enabled using the NARX model. The time-series prediction ensures the effective prediction of the rainfall in a particular area or the locality based on the rainfall data in the previous term or month or year. The proposed NARX model serves as an adaptive prediction model, for which the rainfall data of the previous period is the input, and the optimal computation is based on the proposed algorithm. The adaptive prediction using the proposed algorithm is exhibited in the NARX, and the proposed algorithm is developed based on the Grey Wolf Optimization and the Levenberg–Marqueret (LM) algorithm. The proposed algorithm inherits the advantages of both the algorithms with better computational time and accuracy.

The analysis using two databases enables the better understanding of the proposed rainfall detection methods and proves the effectiveness of the proposed prediction method. The effectiveness of the proposed method is enhanced and the accuracy is found to be better compared with the other existing methods and the mean square error and percentage root mean square difference of the proposed method are found to be around 0.0093 and 0.207.

The rainfall prediction is enabled adaptively using the proposed Grey Wolf Levenberg–Marquardt (GWLM)-based NARX, wherein an algorithm, named GWLM, is proposed by the integration of Grey Wolf Optimizer and LM algorithm.

To provide an autonomous navigation system to endow lunar rovers with increased autonomy both for exploration achievement of scientific goals and for safe navigation.

First, algorithm and technique of initial position determination of lunar rovers are introduced. Then, matched‐features set is build by multi steps of image processing such as feature detection, feature tracking and feature matching. Based on the analysis of the image processing error, a two‐stage estimation algorithm is used to estimate the motion, robust linear motion estimation is executed to estimate the motion initially and to reject the outliers, and Levenberg‐Marquardt non‐linear estimation is used to estimate the motion precisely. Next, a weighted ZSSD algorithm is presented to estimate the image disparities by analyzing the traditional ZSSD. Finally, a virtual simulation system is constructed using the development tool of open inventor, this simulation system can provide stereo images for simulations of stereo vision and motion estimation techniques, simulation results are provided and future research work is addressed in the end.

An autonomous navigation system is build based on stereo vision, the motion estimation algorithm and disparity estimation algorithm are developed.

The field test will be done in the near future to valid the autonomous navigation algorithm presented in this paper.

A very useful source of information for graduate students and technical reference for researchers who work on lunar rovers.

In this paper, stereo vision‐based autonomous navigation techniques for lunar rovers are discussed, and an autonomous navigation scheme which based on stereo vision is presented, and the validity of all the algorithms involved is confirmed by simulations.

This paper attempts to develop an efficient algorithm to solve the inverse problem of identifying constitutive parameters in VFG (viscoelastic functionally graded) materials/structures.

An adaptive recursive algorithm with high fidelity is developed to acquire the derivatives of displacements with respect to constitutive parameters, which are required for the accurate and stable gradient based inverse analysis. A two-step strategy is presented in the process of identification, by which the unknown parameters can be separately identified and the scale and complexity of the inverse VFG problem are reduced. At each step, the process of identification is treated as an optimization problem that is solved by the Levenberg–Marquardt method.

The solution accuracy of forward problems and derivatives of displacements can be stably achieved with different step sizes, and constitutive parameters of homogenous/regional-inhomogeneous VFG materials/structures can be effectively and accurately identified. By examining the reliability, resolution, impacts of reference information and noisy data, the effectiveness of the proposed approach is numerically verified via three numerical examples.

An adaptive recursive algorithm is developed for derivatives computing with high fidelity, providing a solid platform for the sensitivity analysis and thereby a two-step strategy in conjunction with Levenberg–Marquardt method is presented in the process of identification. Consequently, an effective algorithm is developed to identify constitutive parameters of homogenous/regional-inhomogeneous VFG materials/structures.

Mesh Parameterization is central to reverse engineering, tool path planning, etc. This work synthesizes parameterizations with un-constrained borders, overall minimum angle plus area distortion. This study aims to present an assessment of the sensitivity of the minimized distortion with respect to weighed area and angle distortions.

A Mesh Parameterization which does not constrain borders is implemented by performing: isometry maps for each triangle to the plane Z = 0; an affine transform within the plane Z = 0 to glue the triangles back together; and a Levenberg–Marquardt minimization algorithm of a nonlinear F penalty function that modifies the parameters of the first two transformations to discourage triangle flips, angle or area distortions. F is a convex weighed combination of area distortion (weight: α with 0 ≤ α ≤ 1) and angle distortion (weight: 1 − α).

The present study parameterization algorithm has linear complexity [𝒪(n), n = number of mesh vertices]. The sensitivity analysis permits a fine-tuning of the weight parameter which achieves overall bijective parameterizations in the studied cases. No theoretical guarantee is given in this manuscript for the bijectivity. This algorithm has equal or superior performance compared with the ABF, LSCM and ARAP algorithms for the Ball, Cow and Gargoyle data sets. Additional correct results of this algorithm alone are presented for the Foot, Fandisk and Sliced-Glove data sets.

The devised free boundary nonlinear Mesh Parameterization method does not require a valid initial parameterization and produces locally bijective parameterizations in all of our tests. A formal sensitivity analysis shows that the resulting parameterization is more stable, i.e. the UV mapping changes very little when the algorithm tries to preserve angles than when it tries to preserve areas. The algorithm presented in this study belongs to the class that parameterizes meshes with holes. This study presents the results of a complexity analysis comparing the present study algorithm with 12 competing ones.

The purpose of this paper is to focus on the performance of three typical nonlinear least-squares estimation algorithms in atmospheric density model calibration.

The error of Jacchia-Roberts atmospheric density model is expressed as an objective function about temperature parameters. The estimation of parameter corrections is a typical nonlinear least-squares problem. Three algorithms for nonlinear least-squares problems, Gauss–Newton (G-N), damped Gauss–Newton (damped G-N) and Levenberg–Marquardt (L-M) algorithms, are adopted to estimate temperature parameter corrections of Jacchia-Roberts for model calibration.

The results show that G-N algorithm is not convergent at some sampling points. The main reason is the nonlinear relationship between Jacchia-Roberts and its temperature parameters. Damped G-N and L-M algorithms are both convergent at all sampling points. G-N, damped G-N and L-M algorithms reduce the root mean square error of Jacchia-Roberts from 20.4% to 9.3%, 9.4% and 9.4%, respectively. The average iterations of G-N, damped G-N and L-M algorithms are 3.0, 2.8 and 2.9, respectively.

This study is expected to provide a guidance for the selection of nonlinear least-squares estimation methods in atmospheric density model calibration.

The study analyses the performance of three typical nonlinear least-squares estimation methods in the calibration of atmospheric density model. The non-convergent phenomenon of G-N algorithm is discovered and explained. Damped G-N and L-M algorithms are more suitable for the nonlinear least-squares problems in model calibration than G-N algorithm and the first two algorithms have slightly fewer iterations.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

This paper aims to propose a new upper limb movement classification with two phases like pre-processing and classification. Investigation of human limb movements is a significant topic in biomedical engineering, particularly for treating patients. Usually, the limb movement is examined by analyzing the signals that occurred by the movements. However, only few attempts were made to explore the correlations among the movements that are recognized by the human brain.

The initial process is the pre-processing that is performed for detecting and removing noisy channels. The artifacts are marked by band-pass filtering that discovers the values below and above thresholds of 200 and –200 µV, correspondingly. It also discovers the trials with unusual joint probabilities, and the trials with unusual kurtosis are also determined using this method. After this, the pre-processed signals are subjected to a classification process, where the neural network (NN) model is used. The model finally classifies six movements like “elbow extension, elbow flexion, forearm pronation, forearm supination, hand open, and hand close,” respectively. To make the classification more accurate, this paper intends to optimize the weights of NN by a new hybrid algorithm known as bypass integrated jaya algorithm (BI-JA) that hybrids the concept of rider optimization algorithm (ROA) and JA. Finally, the performance of the proposed model is proved over other conventional models concerning certain measures like accuracy, sensitivity, specificity, and precision, false positive rate, false negative rate, false discovery rate, F₁-score and Matthews correlation coefficient.

From the analysis, the adopted BI-JA-NN model in terms of accuracy was high at 80th population size was 7.85%, 3.66%, 7.53%, 2.09% and 0.52% better than Levenberg–Marquardt (LM)-NN, firefly (FF)-NN, JA-NN, whale optimization algorithm (WOA)-NN and ROA-NN algorithms. On considering sensitivity, the proposed method was 2%, 0.2%, 5.01%, 0.29% and 0.3% better than LM-NN, FF-NN, JA-NN, WOA-NN and ROA-NN algorithms at 50th population size. Also, the specificity of the implemented BI-JA-NN model at 80th population size was 7.47%, 4%, 7.05%, 2.1% and 0.5% better than LM-NN, FF-NN, JA-NN, WOA-NN and ROA-NN algorithms. Thus, the betterment of the presented scheme was proved.

This paper adopts the latest optimization algorithm called BI-JA to introduce a new upper limb movement classification with two phases like pre-processing and classification. This is the first work that uses BI-JA based optimization for improving the upper limb movement detection using electroencephalography signals.

Most of the redundant dual-arm robots are singular free, dexterous and collision free compared to other robotic arms. This paper aims to analyse the workspace of redundant arms to study the manipulability. Furthermore, multi-layer perceptron (MLP) algorithm is used to determine the various joint parameters of both the upper body redundant arms. Trajectory planning of robotic arms is carried out with the help of inverse solutions obtained from the MLP algorithm.

In this paper, the kinematic equations are derived from screw theory approach and inverse kinematic solutions are determined using MLP algorithm. Levenberg–Marquardt (LM) and Bayesian regulation (BR) techniques are used as the backpropagation algorithms. The results from two backpropagation techniques are compared for determining the prediction accuracy. The inverse solutions obtained from the MLP algorithm are then used to optimize the cubic spline trajectories planned for avoiding collision between arms with the help of convex optimization technique. The dexterity of the redundant arms is analysed with the help of Cartesian workspace of arms.

Dexterity of redundant arms is analysed by studying the voids and singular spaces present inside the workspace of arms. MLP algorithms determine unique solutions with less computational effort using BR backpropagation. The inverse solutions obtained from MLP algorithm effectively optimize the cubic spline trajectory for the redundant dual arms using convex optimization technique.

Most of the MLP algorithms used for determining the inverse solutions are used with LM backpropagation technique. In this paper, BR technique is used as the backpropagation technique. BR technique converges fast with less computational time than LM method. The inverse solutions of arm joints for traversing optimized cubic spline trajectory using convex optimization technique are computed from the MLP algorithm.

The purpose of this paper is to summarize several identification methods for the automatic commissioning of electrical drives that are presented in different earlier papers of the same authors. This paper is intended as a contribution to the development of expert systems, taking into account parametric models of the mechanical and electrical subsystem as well as the corresponding parameter fitting.

Some system parameters, which are mandatory for the commissioning of electrical and mechanical systems are often not known. For their identification, a method based on the frequency response calculation utilizing the Welch method is now presented. The main focus of the work is directed to the measurement of the frequency response by exciting the system with pseudo‐random binary signals and to the subsequent procedure for the calculation of the corresponding parameter by utilizing the Levenberg‐Marquardt algorithm.

The presented identification procedure leads to outstanding results during the commissioning of the system as well as under normal operation conditions. The identification of the parameter of the mechanical and electrical systems is therefore possible during the commissioning of the drive as well as in running machines. Further, some restrictions regarding the measurement facilities are presented.

The presented identification procedure can be applied in a variety of conditions and can be applied for diagnostic tasks. New measurement and considerations regarding the restrictions of the applied method also under normal operation of the systems underline this fact.