High-resolution pseudo-inverse ghost imaging

We present a pseudo-inverse ghost imaging(PGI) technique which can dramatically enhance the spatial transverse resolution of pseudo-thermal ghost imaging(GI). In comparison with conventional GI, PGI can break the limitation on the imaging resolution imposed by the speckle’s transverse size on the object plane and also enables the reconstruction of an N-pixel image from much less than N measurements. This feature also allows high-resolution imaging of gray-scale objects. Experimental and numerical data assessing the performance of the technique are presented.

Optimal Thrust Allocation Logic Design of Dynamic Positioning with Pseudo-Inverse Method

This article presents a method named pseudo-inverse to solve the optimal thrust allocation of dynamic positioning (DP) system,proposes to optimally determine the azimuth angle of thrusters instead of man-control or semi-auto control,and combines with the pseudo-inverse methods to get the optimal solutions for dynamic positioning control system.It is able to greatly reduce the risk of manual mode.Three different kinds of modes are proposed and detailedly illuminated,and can be used to solve much more complex nonlinear constraint problems,such as typical forbidden vector boundary.Several illustrative examples are provided to demonstrate the effectiveness and correctness of the proposed thrust allocation modes.

Inverses and Pseudo-Inverses of a Strong Endomorphism of a Graph

In this paper,we explicitly describe all the inverses and pseudo-inverses of a strong endomorphism of a graph.The number of them is determined.In addition,we give a characterization of a strong endomorphism whose pseudo-inverse set coincides with its inverse set.The graph,each strong endomorphism of which has this property,is also investigated.

A Modified Eigenvector Method for Blind Deconvolution of MIMO Systems Using the Matrix Pseudo-Inversion Lemma

Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculating the eigenvectors of a matrix relevant to it. However, the performance accuracy of the EVM depends highly on computational results of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Moreover, using a combination of the conventional EVM and the modified EVM, we will show its performances comparing with each EVM. Simulation results will be presented for showing the effectiveness of the proposed methods.

Control allocation algorithm for over-actuated electric vehicles

A control allocation algorithm based on pseudo-inverse method was proposed for the over-actuated system of four in-wheel motors independently driving and four-wheel steering-by-wire electric vehicles in order to improve the vehicle stability. The control algorithm was developed using a two-degree-of-freedom(DOF) vehicle model. A pseudo control vector was calculated by a sliding mode controller to minimize the difference between the desired and actual vehicle motions. A pseudo-inverse controller then allocated the control inputs which included driving torques and steering angles of the four wheels according to the pseudo control vector. If one or more actuators were saturated or in a failure state, the control inputs are re-allocated by the algorithm. The algorithm was evaluated in Matlab/Simulink by using an 8-DOF nonlinear vehicle model. Simulations of sinusoidal input maneuver and double lane change maneuver were executed and the results were compared with those for a sliding mode control. The simulation results show that the vehicle controlled by the control allocation algorithm has better stability and trajectory-tracking performance than the vehicle controlled by the sliding mode control. The vehicle controlled by the control allocation algorithm still has good handling and stability when one or more actuators are saturated or in a failure situation.

Design of Hanman Entropy Network from Radial Basis Function Network

Different learning algorithms have been developed in the literature for training the radial basis function network (RBFN). In this paper, a new neural network named as Hanman Entropy Network (HEN) is developed from RBFN based on the Information set theory that deals with the representation of possibilistic uncertainty in the attribute/property values termed as information source values. The parameters of both HEN and RBFN are learned using a new learning algorithm called JAYA that solves the constrained and unconstrained optimization problems and is bereft of algorithm-specific parameters. The performance of HEN is shown to be superior to that of RBFN on four datasets. The advantage of HEN is that it can use both information source values and their membership values in several ways whereas RBFN uses only the membership function values.

A Double-Weighted Deterministic Extreme Learning Machine Based on Sparse Denoising Autoencoder and Its Applications

Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on Sparse Denoising AutoEncoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.

Adaptive Reconfiguration for Launch Vehicle Under Servo Mechanism Fault

This paper investigates the servo mechanism reconfiguration and fault tolerance control issue for a launch vehicle.Firstly,the servo reconfiguration algorithm is considered as an optimization model,and commonly used optimization algorithms are analyzed and compared.An improved method based on Singular Value Decomposition(SVD)for solving the suboptimal solution of the direct assignment problem is proposed,being suitable for engineering application,while maintaining the advantages of existing algorithms.Theoretical analysis and simulation results confirm that the proposed method is able to provide the optimal reconfiguration strategy with higher computational efficiency.Finally,the numerical simulation of launch vehicle fault tolerance control fully verifies the feasibility and effectiveness of the improved method,which indicates that the method met the engineering application conditions.

Convergence of Hybrid Steepest-Descent Methods for Generalized Variational Inequalities

In this paper, we consider the generalized variational inequality GVI（F, g, C）, where F and g are mappings from a Hilbert space into itself and C is the fixed point set of a nonexpansive mapping. We propose two iterative algorithms to find approximate solutions of the GVI（F,g, C）. Strong convergence results are established and applications to constrained generalized pseudo-inverse are included.