Application of fuzzy logic and neural networks for dynamic travel time estimation

The effectiveness of loop detectors as a data source for advanced traveler information systems has been researched recently [V. P. Sisiopiku (1993 Travel Time Estimation from Loop Detector Data for Advanced Traveler Information System Applications , Ph.D. Thesis, University of Illinois at Chicago]. In urban traffic control schemes loop detectors provide on-line information on traffic conditions consisting of volume counts and occupancy levels. The need to convert loop detector data into travel times is recognized mostly in data fusion applications [P. Nelson and P. Palacharla (1993) A neural network model for data fusion in ADVANCE, Pacific Rim Transportation Technology Conference Proceedings , Vol. I, pp. 237–243, Seattle, WA, 1993]. Literature review indicates limited knowledge on the actual relationship between travel times and loop detector data under interrupted traffic conditions [V. P. Sisiopiku and N. M. Rouphail (1994) Towards the Use of Detector Output for Arterial Link Travel Time Estimation: a Literature Review. Transportation Research Record Series , Washington, DC]. Currently available statistical regression models cannot capture the dynamics of traffic conditions under signalized control and suffer from limited calibration and empirical validation. This paper presents a fuzzy reasoning model to convert loop detector data into link travel times obtained from empirical studies. This model incorporates flexible reasoning and captures non-linear relationship between link specific detector data and travel times.     

Neural network model for rapid forecasting of freeway link travel time

Estimation of freeway travel time with reasonable accuracy is essential for successful implementation of an advanced traveler information system (ATIS) for use in an intelligent transportation system (ITS). An ATIS consists of a route guiding system that recommends the most suitable route based on the traveler's requirements using the information gathered from various sources such as loop detectors and probe vehicles. This information can be disseminated through mass media or on on-board satellite-based navigational system. Based on the estimated travel times for various routes, the traveler can make a route choice. In this article, a neural network model is presented for forecasting the freeway link travel time using the counter propagation neural (CPN) network. The performance of the model is compared with a recently reported freeway link travel forecasting model using the backpropagation (BP) neural network algorithm. It is shown that the new model based on the CPN network, and the learning coefficients proposed by Adeli and Park, is nearly two orders of magnitude faster than the BP network. As such, the proposed freeway link travel-forecasting model is particularly suitable for real-time advanced travel information and management systems.     

Discrete time congestion controllers for multi-source connection-oriented communication networks

In this article, a new, discrete time, non-linear flow control strategy for a connection-oriented, multi-source communication network is proposed. The strategy effectively exploits the Smith principle in order to avoid data loss in the network, and to ensure full utilisation of the bottleneck link available bandwidth. The desirable properties of the proposed control strategy are preserved, even though the connection round trip times may be determined imprecisely. Furthermore, an enhanced strategy, which employs extra feed-forward bandwidth compensation, and reduces the influence of the available bandwidth variations on the steady state queue length in the network, is introduced. Finally, the proposed strategy is modified to be appropriate for application when the number of active connections changes during the control process, and new conditions for no data loss and full bandwidth utilisation are formulated and strictly proved. Since the modified strategy allows for arbitrary resource allocation among the controlled virtual circuits, the max–min fairness criteria can be satisfied.     

Application of neural networks for predicting program faults

Accurately predicting the number of faults in program modules is a major problem in the quality control of large software development efforts. Some software complexity metrics are closely related to the distribution of faults across program modules. Using these relationships, software engineers develop models that provide early estimates of quality metrics that do not become available until late in the development cycle. By considering these early estimates, software engineers can take actions to avoid or prepare for emerging quality problems. Most often, the predictive models are based upon multiple regression analysis. However, measures of software quality and complexity exhibit systematic departures from the assumptions of these analyses. With extreme violations of these assumptions, multiple regression models become unstable and lose most of their predictive quality. Since neural network models carry no data assumptions, these models could be more appropriate than regression models for modeling software faults. In this paper, we explore a neural network methodology for developing models that predict the number of faults in program modules. We apply this methodology to develop neural network models based upon data collected during the development of two commercial software systems. After developing neural network models, we apply multiple linear regression methods to develop regression models on the same data. For the data sets considered, the neural network methodology produced better predictive models in terms of both quality of fit and predictive quality.     

Classification of freeway traffic patterns for incident detectionusing constructive probabilistic neural networks

This paper proposes a new technique for freeway incident detection using a constructive probabilistic neural network (CPNN). The CPNN incorporates a clustering technique with an automated training process. The work reported in this paper was conducted on Ayer Rajah Expressway (AYE) in Singapore for incident detection model development, and subsequently on I-880 freeway in California, for model adaptation. The model developed achieved incident detection performance of 92% detection rate and 0.81% false alarm rate on AYE, and 91.30% detection rate and 0.27% false alarm rate on I-880 freeway using the proposed adaptation method. In addition to its superior performance, the network pruning method employed facilitated model size reduction by a factor of 11 compared to a conventional probabilistic neural network. A more impressive size reduction by a factor of 50 was achieved after the model was adapted for the new site. The results from this paper suggest that CPNN is a better adaptive classifier for incident detection problem with a changing site traffic environment.     

Adaptive wave variables for bilateral teleoperation using neural networks

Stability and transparency determine the performance of bilateral teleoperation systems. Previous studies on passivity-based control focused on stability such that the results of the study are robust in terms of the time delay issue. But there are not sufficient studies on performance analysis based on environmental elements related to transparency. This paper suggests an adaptive wave transformation system where stability is secured by controlling characteristic impedance in the existing wave variables system adaptively according to time delay and environmental elements and simultaneously ensuring a proper dynamic performance depending on external force. Neural network was utilized to design the system that enables controlling the characteristic impedance depending on external factors such as time delay and comparison with the existing wave variables.     

Projective synchronization for fractional-order memristor-based neural networks with time delays

In this paper, the global projective synchronization for fractional-order memristor-based neural networks with multiple time delays is investigated via combining open loop control with the time-delayed feedback control. A comparison theorem for a class of fractional-order systems with multiple time delays is proposed. Based on the given comparison theorem and Lyapunov method, the synchronization conditions are derived under the framework of Filippov solution and differential inclusion theory. Several feedback control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization for the fractional-order memristor-based neural networks with time delays. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.

     

Global stability for cellular neural networks with time delay.

A sufficient condition related to the existence of a unique equilibrium point and its global asymptotic stability for cellular network networks with delay (DCNNs) is derived. It is shown that the condition relies on the feedback matrices and is independent of the delay parameter. Furthermore, this condition is less restrictive than that given in the literature.     

Self-learning fuzzy neural networks for control of uncertainsystems with time delays

We address the problem of control of uncertain systems with time delays. Using the fuzzy logic control and artificial neural network methodologies, we present a self-learning fuzzy neural control scheme for general uncertain processes. In this scheme, a neural network compensator is designed instead of the classical Smith predictor for attenuating the adverse effects of time delays of the uncertain systems. The scheme has been used in control of welding pool dynamics of the arc welding process, and the experiment results show the control scheme available.     

Nonlinear time series forecasting with Bayesian neural networks

The Bayesian learning provides a natural way to model the nonlinear structure as the artificial neural networks due to their capability to cope with the model complexity. In this paper, an evolutionary Monte Carlo (MC) algorithm is proposed to train the Bayesian neural networks (BNNs) for the time series forecasting. This approach called as Genetic MC is based on Gaussian approximation with recursive hyperparameter. Genetic MC integrates MC simulations with the genetic algorithms and the fuzzy membership functions. In the implementations, Genetic MC is compared with the traditional neural networks and time series techniques in terms of their forecasting performances over the weekly sales of a Finance Magazine.     

Simultaneous recurrent neural network trained with non-recurrent backpropagation algorithm for static optimisation

This paper explores feasibility of employing the non-recurrent backpropagation training algorithm for a recurrent neural network, Simultaneous Recurrent Neural network, for static optimisation. A simplifying observation that maps the recurrent network dynamics, which is configured to operate in relaxation mode as a static optimizer, to feedforward network dynamics is leveraged to facilitate application of a non-recurrent training algorithm such as the standard backpropagation and its variants. A simulation study that aims to assess feasibility, optimizing potential, and computational efficiency of training the Simultaneous Recurrent Neural network with non-recurrent backpropagation is conducted. A comparative computational complexity analysis between the Simultaneous Recurrent Neural network trained with non-recurrent backpropagation algorithm and the same network trained with the recurrent backpropagation algorithm is performed. Simulation results demonstrate that it is feasible to apply the non-recurrent backpropagation to train the Simultaneous Recurrent Neural network. The optimality and computational complexity analysis fails to demonstrate any advantage on behalf of the non-recurrent backpropagation versus the recurrent backpropagation for the optimisation problem considered. However, considerable future potential that is yet to be explored exists given that computationally efficient versions of the backpropagation training algorithm, namely quasi-Newton and conjugate gradient descent among others, are also applicable for the neural network proposed for static optimisation in this paper.     

Stationary oscillation for cellular neural networks with time delays and impulses

This paper studies stationary oscillation for a time-varying recurrent cellular neural network with time delays and impulses. In a recent paper, the authors claim that they obtain a criterion of existence, uniqueness, and global exponential stability of periodic solution (i.e. stationary oscillation) for a recurrent cellular neural network with time delays and impulses. We point out that the main result of their paper is incorrect, and present a sufficient condition of stationary oscillation for a time-varying recurrent cellular neural networks with time delays and impulses. An numerical example is given to illustrate the effectiveness of the obtained result.     

Multiple asymptotical stability analysis for fractional-order neural networks with time delays

This paper formulates the multiple asymptotical stability for a general class of fractional-order neural networks with time delays. By exploiting the properties of upper bounded and lower bounded functions derived from the addressed fractional-order neural network model as well as the comparison principle for fractional-order calculus, a lot of sufficient conditions are obtained to guarantee the existence and multiple asymptotical stability of the equilibrium points for the fractional-order neural networks with time delays. It reveals that the results gained in this paper are applicable to analyses of both multiple asymptotical stability and global asymptotical stability. Besides, three numerical examples are presented to showcase the validity of the derived results.     

New delay-dependent exponential stability for neural networks with time delay.

In this correspondence, the problem of exponential stability for neural networks with time delay is investigated. By introducing a novel Lyapunov-Krasovskii functional with the idea of delay fractioning, a new criterion of exponential stability is derived and then formulated in terms of a linear matrix inequality. This new criterion proves to be much less conservative than the most recent result, and the conservatism can be notably reduced as the fractioning goes thinner. An example is provided to demonstrate the advantage of the proposed result.     

Delay-dependent stability analysis for impulsive neural networks with time varying delays

In this paper, the global exponential stability and global asymptotic stability of the neural networks with impulsive effect and time varying delays is investigated. By using Lyapunov–Krasovskii-type functional, the quality of negative definite matrix and Cauchy criterion, we obtain the sufficient conditions for global exponential stability and global asymptotic stability of such model, in terms of linear matrix inequality (LMI), which depend on the delays. Two examples are given to illustrate the effectiveness of our theoretical results.     

Robust stability for interval Hopfield neural networks with time delay.

The conventional Hopfield neural network with time delay is intervalized to consider the bounded effect of deviation of network parameters and perturbations yielding a novel interval dynamic Hopfield neural network (IDHNN) model. A sufficient condition related to the existence of unique equilibrium point and its robust stability is derived.     

Less conservative results of state estimation for delayed neural networks with fewer LMI variables

In this paper, the state estimation problem is investigated for neural networks with time-varying delays as well as general activation functions. By applying the Finsler's Lemma and constructing appropriate Lyapunov-Krasovskii functional based on delay partitioning, several improved delay-dependent conditions are developed to estimate the neuron state with some available output measurements such that the error-state system is global asymptotically stable. It is established theoretically that one special case of the obtained criteria is equivalent to some existing result with same conservatism but including fewer LMI variables. As the present conditions involve no free-weighting matrices, the computational burden is largely reduced. Two examples are provided to demonstrate the effectiveness of the theoretical results.     

动态非线性连续时间系统的小波神经网络辨识

将小波神经网络应用于动态非线性连续时间系统的辨识, 同时为了使神经网络的训练达到全局最优和加速小波神经网络训练的收敛速度, 提出了信赖域算法, 并研究了信赖域算法的收敛性. 随后进行了算例仿真, 证明了所提辨识方法的有效性.     

Residential property price time series forecasting with neural networks

The residential property market accounts for a substantial proportion of UK economic activity. Professional valuers estimate property values based on current bid prices (open market values). However, there is no reliable forecasting service for residential values with current bid prices being taken as the best indicator of future price movement. This approach has failed to predict the periodic market crises or to produce estimates of long-term sustainable value (a recent European Directive could be leading mortgage lenders towards the use of sustainable valuations in preference to the open market value). In this paper, we present artificial neural networks, trained using national housing transaction time series data, which forecasts future trends within the housing market.