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Visual recognition of crowd dynamics has had a huge impact on several applications including surveillance, situation awareness, homeland security and intelligent environments. However, the state-of-the-art in crowd analysis has become diverse due to factors such as: (a) the underlying definition of a crowd, (b) the constituent elements of the crowd processing model, (c) its application, hence (d) the dataset and (e) the evaluation criteria available for benchmarking. Although such diversity is healthy, the techniques for crowd modelling thus developed have failed to establish credibility therefore becoming unreliable and of questionable validity across different research disciplines. This paper aims to give an account of such issues by deducing key statistical evidence from the existing literature and providing recommendations towards focusing on the general aspects of techniques rather than any specific algorithm. The advances and trends in crowd analysis are drawn in the context of crowd modelling studies published in leading journals and conferences over the past 7 years. Finally, this paper shall also provide a qualitative and quantitative comparison of some existing methods using various publicly available crowd datasets to substantiate some of the theoretical claims. [ABSTRACT FROM AUTHOR]

This paper introduces an effective approach to study the stability of static neural networks with interval time-varying delay using delay partitioning approach and tighter integral inequality lemma. By decomposing the delay interval into multiple equidistant subintervals and multiple nonuniform subintervals, some suitable Lyapunov–Krasovskii functionals are constructed on these intervals. A set of novel sufficient conditions are obtained to guarantee the stability analysis issue for the considered system. These conditions are expressed in the framework of linear matrix inequalities, which heavily depend on the lower and upper bounds of the time-varying delay. It is shown, by comparing with existing approaches, that the delay-partitioning approach can largely reduce the conservatism of the stability results. Finally, three examples are given to show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problems of finite-time filter analysis and design for discrete time-delay neural networks with Markovian jump parameters and sector-bounded activation functions represented by Takagi–Sugeno fuzzy model. Firstly, a sufficient admissibility criterion is presented to guarantee that the augmented fuzzy jump neural network without parameter uncertainties is stochastically finite-time bounded in a prescribed time interval. From the admissibility criterion obtained, a sufficient condition on stochastic finite-time boundedness is provided for the augmented fuzzy jump neural networks. Then, a sufficient criterion to design a finite-time fuzzy filter is presented with uncertain parameters and Markovian jumps for discrete time-delay fuzzy neural networks. Moreover, conditions on stochastic finite-time stability are also established for nominal and uncertain delayed fuzzy jump neural networks without the presence of external disturbance. All criteria obtained can be represented as the form of linear matrix inequalities. Finally, numerical examples are given to illustrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper presents an autopilot design for a robotic unmanned surface vehicle in the presence of unknown yaw dynamics and measurement noises. A robust adaptive steering law is developed with the aid of a predictor, neural networks, and a modified dynamic surface control technique. Specifically, a predictor together with a low-frequency learning-based neural updating law is developed to identify the unknown yaw dynamics, as well as to reconstruct the states corrupted by measurement noises. Besides, to avoid the noise amplification effect of first-order filter, a linear tracking differentiator is incorporated into the dynamic surface control design approach to produce a noise-tolerant estimate of virtual control derivative. The stability of the closed-loop autopilot system is established by Lyapunov analysis. A salient feature of the developed controller is that it can achieve the desired performance in the presence of model uncertainty and measurement noises, simultaneously. Both simulation and experiment results are given to validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the novel architectures׳ formulation and linear matrix inequality (LMI)-based design of dynamic nonlinear anti-windup compensator (AWC) for nonlinear time-delay systems with continuous interval time-varying delays under input saturation. An internal model control (IMC)-based AWC architecture is suggested for stable nonlinear time-delay systems and, in addition, a decoupling AWC architecture, applicable to a broader class of nonlinear time-delay systems, is proposed for compensation of the undesirable saturation effects. Further, a correspondent decoupled architecture is derived and recommended for characterizing the delayed nonlinear AWC synthesis goals. By employing Lyapunov–Krasovskii functional, local sector condition, Lipschitz condition, L 2 gain minimization, and the delay-interval information, several sufficient conditions are derived for the design of nonlinear time-delay AWC. Numerical examples for FitzHugh–Nagumo neuron and Hopfield neural network under input saturation and time-delay are presented to reveal effectiveness of the proposed anti-windup approach. [ABSTRACT FROM AUTHOR]

This paper provides an undulatory locomotion model of C. elegans to achieve the chemotaxis behaviors based on the biological neuronal and neuromuscular structure. The on-cell and off-cell mechanism, as well as the proprioceptive mechanism is incorporated into the locomotion model. The nervous system of C. elegans is modeled by a dynamic neural network (DNN) that involves two parts: head DNN and motor neurons. The head DNN perceives the outside concentrations and generates the undulatory wave to the body. The motor neurons are responsible for transiting the undulatory wave along the body. The body of C. elegans is represented as a multi-joint rigid link model with 11 links. The undulatory locomotion behavior is achieved by using the DNN to control the lengths of muscles on ventral and dorsal sides, and then using the muscle lengths to control the angles between two consecutive links. In this work, the relations between the outputs of DNN and muscle lengths, as well as the muscle lengths and the angles between two consecutive links, are determined. Furthermore, owing to the learning capability of DNN, a set of nonlinear functions that are designed to represent the chemotaxis behaviors of C. elegans are learned by the head DNN. The testing results show good performance of the locomotion model for the chemotaxis behaviors of finding food and avoiding toxin, as well as slight and Ω turns. At last, quantitative analyses by comparing with the experiment results are provided to verify the realness and effectiveness of the locomotion model, which could serve as a prototype for other footless animals. [ABSTRACT FROM AUTHOR]

A unified associative memory model with a novel method for designing associative memories is presented in this paper. Based on continuous recurrent neural networks, bipolar patterns inputted from external can cause the output of neural networks to be memorized patterns. In the method, two conditions relevant to external inputs are derived to ensure the network states converge to a stable interval, and an exponential stable criterion is proposed for the network being a bipolar associative memory with higher recall speed. By introducing a tunable slope activation function and considering time delay, the proposed model is general and can recall the memorized patterns in auto-associative and hetero-associative way, while higher robust and more flexible memory can be obtained through the proposed method. Experimental verification demonstrates the effectiveness and generalization of the proposed method. [ABSTRACT FROM AUTHOR]

Dynamic model is an important issue for research on stability, dynamic characteristics, surge and control technique of axial-flow pumping system, and such a model is usually characterized by complex nonlinearity, strong coupling and time-varying mathematical equation. For the convenience of establishing model and highly effective computing, dynamic characteristics of the whole system are divided into four parts: pump lift-flow characteristics, pipeline characteristics, mechanical characteristics of asynchronous motor and torque characteristics of pump load. Each part is a nonlinear subsystem, and there are complex coupling relations among each other. In the paper, each part of the pump system is modeled respectively by mechanisms of hydrodynamics, transmission dynamics, electromechanics and affinity law. Considering that the axial-flow pump is characterized by nonlinearity and parameters are difficultly estimated in the low flow operation area and that the data of pump head-flow can be easily tested under the speed of power frequency, a modeling method combined with the RBF neural network is proposed, where hidden layer parameters are optimized by K means clustering algorithm, and the weights are trained by least square method. At last the whole simulation model of the axial-flow pumping system is set up, and the validity of the proposed modeling method is verified through simulation. [ABSTRACT FROM AUTHOR]

This paper proposes an improved stochastic second order learning algorithm for supervised neural network training. The proposed algorithm, named bounded stochastic diagonal Levenberg–Marquardt (B-SDLM), utilizes both gradient and curvature information to achieve fast convergence while requiring only minimal computational overhead than the stochastic gradient descent (SGD) method. B-SDLM has only a single hyperparameter as opposed to most other learning algorithms that suffer from the hyperparameter overfitting problem due to having more hyperparameters to be tuned. Experiments using the multilayer perceptron (MLP) and convolutional neural network (CNN) models have shown that B-SDLM outperforms other learning algorithms with regard to the classification accuracies and computational efficiency (about 5.3% faster than SGD on the mnist-rot-bg-img database). It can classify all testing samples correctly on the face recognition case study based on AR Purdue database. In addition, experiments on handwritten digit classification case studies show that significant improvements of 19.6% on MNIST database and 17.5% on mnist-rot-bg-img database can be achieved in terms of the testing misclassification error rates (MCRs). The computationally expensive Hessian calculations are kept to a minimum by using just 0.05% of the training samples in its estimation or updating the learning rates once per two training epochs, while maintaining or even achieving lower testing MCRs. It is also shown that B-SDLM works well in the mini-batch learning mode, and we are able to achieve 3.32 × performance speedup when deploying the proposed algorithm in a distributed learning environment with a quad-core processor. [ABSTRACT FROM AUTHOR]