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Due to the time delay and some unavoidable noise factors, obtaining a real-time solution of dynamic time-varying linear matrix equation (LME) problems is of great importance in the scientific and engineering fields. In this paper, based on the philosophy of zeroing neural networks (ZNN), we propose an integration-enhanced combined accelerating zeroing neural network (IEAZNN) model to solve LME problem accurately and efficiently. Different from most of the existing ZNNs research, there are two error functions combined in the IEAZNN model, among which the gradient of the energy function is the first design for the purpose of decreasing the norm-based error to zero and the second one is adding an integral term to resist additive noise. On the strength of novel combination in two error functions, the IEAZNN model is capable of converging in finite time and resisting noise at the same time. Moreover, theoretical proof and numerical verification results show that the IEAZNN model can achieve high accuracy and fast convergence speed in solving time-varying LME problems compared with the conventional ZNN (CZNN) and integration-enhanced ZNN (IEZNN) models, even in various kinds of noise environments. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of target tracking using k fittest robots in a group of n mobile robots with n>k. We present centralized and distributed coordination models with all-to-all and limited communications, respectively. For the case of all-to-all communication between robots, theoretical analysis is presented to prove the exponential stability of the whole system. In real applications of robotic networks, a robot may only be allowed to exchange information with a limited number of neighbors. In such a limited communication case where centralized quantity is not available, a consensus filter is used to estimate the centralized quantities in a distributed way, and a distributed competitive target tracking is thus achieved. The stability of the distributed control is also proved in theory. Finally, illustrative examples are provided and analyzed to substantiate the efficacy of the proposed models for tracking moving target in a competition manner with all-to-all communications and limited communications. [ABSTRACT FROM AUTHOR]

Energy efficiency is a key concern for wireless sensor nodes, especially for wireless body area network (WBAN) in which sensors operate in close vicinity to, on or even inside a human body. In this paper, we first present a system-level energy consumption model associated with transmission distance d and transmission data rate over on-body wireless communication link. Then, based on the analysis of tradeoff between circuit energy and transmission energy on distance, a threshold distance d_{\rm th} which is responsible for the proportion of transmission energy and circuit energy is derived for energy saving in WBAN. With the case of d\leq d_{\rm th} , since circuit energy is comparable with transmission energy consumption, the total energy consumption can be saved by optimizing the transmission data rate R . Simulation results show that a 59.77% or even more energy saving is achievable using the optimized scheme, compared with baseline scheme. With d>d_{\rm th} , since the total energy consumption is monotonically decreasing with respect to time t$ , an offline algorithm is applied to energy saving by prolonging transmission time within the deadline time. In addition, on the basis of the offline algorithm, a battery-aware transmission approach is presented for WBAN using battery electrochemical property. Experimental results show that, using the presented battery-aware approach, 71.05% and 60.81% energy saving can be obtained, in comparison with the baseline and offline schemes, respectively. [ABSTRACT FROM PUBLISHER]

The core problem with nodes of wireless sensor networks is the limited charge capacity for the power-operated battery. The main concern of this paper is how to extend the battery's lifetime. According to the characteristics and functionality of sensor nodes, the sensor applications are categorized into three distinct types, i.e., the hard real-time applications, the soft real-time applications, and the periodic applications. For the hard real-time applications, a battery-friendly lazy packet scheme is designed to draw smoother and lower current for minimizing battery charge consumption. A battery-friendly local optimization scheme with slack time is exploited and evaluated for the soft real-time applications. For the periodic applications, a battery-aware task–scheduling algorithm is developed, which reschedules tasks of sensor nodes to achieve the battery friendly discharge profile. The simulation results show that the three battery-friendly algorithms perform better in extending lifetime of battery-operated sensor nodes and minimizing battery charge consumption. [ABSTRACT FROM PUBLISHER]

Breast cancer has become one of the leading causes of death in female population due to its high morbidity and mortality. However, the treatment options for benign or malignant tumors are different, which makes the diagnosis of breast cancer important. In this paper, the subset method is proposed to improve the single-output Chebyshev-polynomial neural network (SOCPNN), and the modified SOCPNN has a 100.00% testing accuracy in pattern classification on the Wisconsin breast cancer dataset. Specifically, the subset method generates the optimal number of cross-validation folds and constructs the initial structure of the modified SOCPNN automatically and rapidly, with a group of basis functions based on Chebyshev-polynomials exploited to activate the neural network. In addition, the weights of the hidden-layer neurons are directly determined by the weights-direct-determination (WDD) method. Besides, the optimal structure of the neural network is determined by a growing method. Moreover, to improve the generalization performance, the multi-fold cross-validation algorithm is exploited in the modified SOCPNN. Finally, comparative experiments on the Wisconsin breast cancer dataset are conducted and the results show that the modified SOCPNN has higher accuracy in classification of breast cancer on this dataset compared to the traditional machine learning algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a distributed scheme for the control of multiple redundant manipulators to simultaneously achieve four objectives, i.e., the task to reach global cooperation, joint-physical limits compliance, limited communications among manipulators and optimality in terms of a specified performance index. In addition, corresponding theoretical analyses are provided, which guarantee that, with the communication network being connected, all manipulators can jointly obtain the same desired motion information. Then, the proposed scheme is converted into a quadratic program (QP) formed formulation and solved by a dynamic neural network with rigorously provable convergence. Furthermore, simulations and comparisons are provided to illustrate the effectiveness of the proposed distributed scheme as well as the presented dynamic neural network. [ABSTRACT FROM AUTHOR]