Search

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]

Short term power load forecasting plays an important role in the security of power system. In the past few years, application of artificial neural network (ANN) for short-term load forecasting (STLF) has become a research hotspots. Generalized regression neural network (GRNN) has been proved to be suitable for solving the non-linear problems. And according to the historical load curve, it can be known that STLF is a non-linear problem. Thus, the GRNN was used for STLF in this paper. However, the value of spread parameter σ determines the performance of the GRNN. The fruit fly optimization algorithm with decreasing step size (SFOA) is introduced to select an appropriate spread parameter σ . Combined with the weather factors and the periodicity of short-term load, an effective STLF model based on the GRNN with decreasing step FOA was proposed. Performance of the proposed SFOA-GRNN model is compared with other ANN on the basis of prediction error. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the global exponential stability is discussed for static recurrent neural networks. Without assuming the boundedness, monotonicity and differentiability of the activation functions, a new sufficient condition is obtained to ensure the existence and uniqueness of the equilibrium based on the nonlinear measure. Meanwhile, the condition obtained also guarantees the global exponential stability of the delayed neural networks via constructing a proper Lyapunov functional. The results, which are independent of the time delay, can be checked easily by convex optimization algorithms. In the end of this paper, two illustrative examples are also given to show the effectiveness of our results. [Copyright &y& Elsevier]

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]

Abstract: In this paper, we introduce an advanced architecture of K-means clustering-based polynomial Radial Basis Function Neural Networks (p-RBF NNs) designed with the aid of Particle Swarm Optimization (PSO) and Differential Evolution (DE) and develop a comprehensive design methodology supporting their construction. The architecture of the p-RBF NNs comes as a result of a synergistic usage of the evolutionary optimization-driven hybrid tools. The connections (weights) of the proposed p-RBF NNs being of a certain functional character and are realized by considering four types of polynomials. In order to design the optimized p-RBF NNs, a prototype (center value) of each receptive field is determined by running the K-means clustering algorithm and then a prototype and a spread of the corresponding receptive field are further optimized through running Particle Swarm Optimization (PSO) and Differential Evolution (DE). The Weighted Least Square Estimation (WLSE) is used to estimate the coefficients of the polynomials (which serve as functional connections of the network). The performance of the proposed model and the comparative analysis involving models designed with the aid of PSO and DE are presented in case of a nonlinear function and two Machine Learning (ML) datasets [Copyright &y& Elsevier]

Abstract: This paper emphasises the link between neural model training and its role in model predictive control (MPC) algorithms. This role is of fundamental importance since in MPC at each sampling instant a model is used on-line to calculate predictions of future behaviour of the process and an optimal future control policy. Taking into account this particular function of models in MPC, a training algorithm of neural dynamic models is derived. An example identification problem of a methanol–water distillation process is discussed. The prediction accuracy of models obtained using the described algorithm and the classical backpropagation scheme is compared, which yields one-step ahead predictors. [Copyright &y& Elsevier]

Abstract: Shortest-path problem is well-known optimization problem and has been studied by many authors in recent years. Typically it is solved by using the famous Dijkstra''s algorithm, which would quickly provide a global optimization solution in most instances. However, as the problem scale increases, this method is inefficient and may consume a considerable amount of CPU time. Neural networks, which are massively parallel models, can solve this question easily. This paper presents a novel biological neural network based algorithm for the finding of the shortest path in large scale systems. The start neuron fires first, and then the firing event spreads out through the lateral connections among the neurons, like the propagation of a wave. Then the generated spiking wave spreads at a constant speed so that the time of travel between two neurons is proportional to the path length between them. The computational complexity of the algorithm is only related to the length of the shortest path, and independent of the number of existing paths in the graph. Simulation results show that the proposed method is more efficient than Dijkstra''s in the larger scale systems. [Copyright &y& Elsevier]

Abstract: Spoken human–machine interaction in real-world environments requires acoustic models that are robust to changes in acoustic conditions, e.g. presence of noise. Unfortunately, the popular hidden Markov models (HMM) are not noise tolerant. One way to increase recognition performance is to acquire a small adaptation set of noisy utterances, which is used to estimate a normalization mapping between noisy and clean features to be fed into the acoustic model. This paper proposes an unsupervised maximum-likelihood gradient-ascent training algorithm (instead of the usual least squares regression) for a neural feature adaptation module, properly combined with a hybrid connectionist/HMM speech recognizer. The algorithm is inspired by the so-called “inversion principle”, that prescribes the optimization of the input features instead of the model parameters. Simulation results on a real-world speaker-independent continuous speech corpus of connected Italian digits, corrupted by noise, validate the approach. A small neural net (13 hidden neurons) trained over a single adaptation utterance for one iteration yields a 18.79% relative word error rate (WER) reduction over the bare hybrid, and a 65.10% relative WER reduction over the Gaussian-based HMM. [Copyright &y& Elsevier]