Adaptive neural control for an uncertain robotic manipulator with joint space constraints

In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.     

H∞ state estimation of generalised neural networks with interval time-varying delays

This paper focuses on studying the H_∞ state estimation of generalised neural networks with interval time-varying delays. The integral terms in the time derivative of the Lyapunov–Krasovskii functional are handled by the Jensen’s inequality, reciprocally convex combination approach and a new Wirtinger-based double integral inequality. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable with H_∞ performance. The proposed conditions are represented by linear matrix inequalities. Optimal H_∞ norm bounds are obtained easily by solving convex problems in terms of linear matrix inequalities. The advantage of employing the proposed inequalities is illustrated by numerical examples.     

Performance of multiple access CSK communication system with DF cooperative strategy

A multiple access chaos shift keying cooperative communication (MA-CSK-CC) system over Rayleigh fading channels and in the presence of Gaussian white noise is investigated in depth in this paper. The simplified half-duplex with a single relay decode-and-forward (DF) cooperative strategy is proposed to illustrate and analysis as an example. Exact and approximation bit-error-ratio (BER) performance expressions are derived. Simulation results demonstrate that the proposed system has a prominent advantage of high BER performance over the conventional multiple access CSK systems that have a single path to transmit signal at the same data rate over Rayleigh fading channels with additive Gaussian white noise. Moreover, based on the BER performance analysis, some useful results are also derived such as the relationships between BER versus spread spectrum factors, BER versus the number of users, etc.     

A systematic investigation of geostatistical image fusion for the improvement of the spectral fidelity and spatial detail in Landsat MS imagery

This is the first systematic investigation into the assumptions of image fusion using regression Kriging (RK) – a geostatistical method – illustrated with Landsat MS (multispectral) and SPOT (Satellite Pour l’Observation de la Terre) panchromatic images. The efficiency of different linear regression and Kriging methods in the fusion process is examined by visual and quantitative indicators. Results indicate a trade-off between spectral fidelity and spatial detail preservation for the GLS (generalized least squares regression) and OLS (ordinary least squares regression) methods in the RK process: OLS methods preserve more spatial detail, while GLS methods retain more spectral information from the MS images but at a greater computational cost. Under either OK (ordinary Kriging) or UK (universal Kriging) with either OLS or GLS, the spherical variogram improves spatial details from the panchromatic image, while the exponential variogram maintains more spectral information from the MS image. Overall, RK-based fusion methods outperform conventional fusion approaches from both the spectral and spatial point of view.     

Learning user credibility for product ranking

As the explosion of user-generated data (UGC) in electronic commerce, this kind of data is scanned for trust or credibility calculation, which plays an important role in business selection. The commonly used UGC is user reviews and ratings. A new consumer without any experience with some product will read these UGCs to get an overview. However, the open and dynamic e-commerce platforms may rise the generation of unfair or deceitful reviews and ratings. Then, detecting trustful reviewers or generating authentic ratings for customers is urgent and useful. In this paper, we present a twin-bipartite graph model to catch the review and ranking relationship among users, products and shops. We design a feedback mechanism to get the consistent ranking among different level of objects, which are users and items. In the algorithm, we adjust customer credibility values by the feedback considering the rating consistency; we adjust ratings by combining customer credibility together with originally assigned ratings. We increase the credibility for a customer if the customer gives a high (low) score to a good (bad) product and decrease the value if the customer gives a low (high) score to a good (bad) product. We detect the inconsistency between semantic ratings (the review comments) and numerical ratings (scores). To deal with it, we train a classifier on the training data that are constructed automatically. The trained classifier is used to predict the semantic scores from review comments. Finally, we calculate the scores of products by considering both the customer credibility and the predicted scores. We conduct experiments using a large amount of real-world data. The experimental results show that our proposed approach provides better products ranking than the baseline systems.     

Error Analysis Methods for Group Decision Making Based on Hesitant Fuzzy Preference Relation

Hesitant fuzzy preference relation (HFPR) is an effective way to depict the decision makers’ preferences over the objects (alternatives or attributes) in the process of group decision making. Each component of the HFPR is characterized by several possible values and can express the decision makers’ hesitant information comprehensively. To make a decision with the HFPR, it is very necessary to find a proper technique for deriving the priority weights from the HFPR. In this paper, we use the error analysis as a tool to develop several straightforward methods for the priorities of the HFPR. We first define the expected value and the average value of each hesitant fuzzy element in the HFPR. Then based on the error analysis, we come up with the interval midpoint method, the average value method, and the difference method to derive the priority weights from the HFPR. After that, we discuss the relations among these methods, and utilize them and the possibility degree formula to develop an approach to decision making with the HFPR. Finally, we demonstrate the effectiveness and practicality of our approach through a case study concerning the investment problem in liquor enterprise.