A new era of automatic train control has begun, in which mass transit trains will be commanded with precision beyond the capabilities of past systems. Although transit properties such as San Francisco's Bay Area Rapid Transit (BART) have controlled their trains automatically for decades, the control systems have limited capability. Increases in capacity now require trains to run closer together...

Using the Karpman-Solov'ev method we derive the equations for the two-soliton adiabatic interaction for solitons of the modified nonlinear Schrödinger equation (MNSE). Then we generalize these equations to the case of N interacting solitons with almost equal velocities and widths. On the basis of this result we prove that the N MNSE-soliton train interaction (N>2) can be modeled by the complete...

Grey-box’ models take advantage of physical insight about the dynamical system in question. This reduces uncertainty and facilitates the remaining system identification process. Here a grey-box system identification (GBSI) method for nonlinear dynamical systems is presented, which first establishes a linear model (the ‘white box’) using a priori knowledge and conventional tools, and then adds a...

The component classification and potential fault region locating in the full-automatic inspection system of a freight train require a computer vision method with the ability of classifying quickly and locating precisely, addressing anti-nonlinear deformations, and being able to perform extensible learning. Inspired by these requirements, this paper specifically optimizes the three elements of a...

Train energy saving problem investigates how to control train's velocity such that the quantity of energy consumption is minimized and some system constraints are satis ed. On the assumption that the train's weights on different links are estimated by fuzzy variables when making the train scheduling strategy, we study the fuzzy train energy saving problem. First, we propose a fuzzy energy ...

The main contribution of this letter is the derivation of a steepest gradient descent learning rule for a multilayer network of theta neurons, a one-dimensional nonlinear neuron model. Central to our model is the assumption that the intrinsic neuron dynamics are sufficient to achieve consistent time coding, with no need to involve the precise shape of postsynaptic currents; this assumption depa...

We study, in terms of synchronization, the nonlinear response of noisy bistable systems to a stochastic external signal, represented by Markovian dichotomic noise. We propose a general kinetic model which allows us to conduct a full analytical study of the nonlinear response, including the calculation of cross-correlation measures, the mean switching frequency, and synchronization regions. Theo...

Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enab...

In this paper, an extended linearized neural state space (ELNSS) topology is proposed, where an ELNSS based modeling and cont rol strategy for a class of nonlinear systems is presented. In terms of the modeling, the extended Kalman filter (EKF) algorithm is used to train the parameters inside the ELNSS model, where a high order correlation method is applied to validate the estimated model. This...

Previous biophysical modeling work showed that nonlinear interactions among nearby synapses located on active dendritic trees can provide a large boost in the memory capacity of a cell (Mel, 1992a, 1992b). The aim of our present work is to quantify this boost by estimating the capacity of (1) a neuron model with passive dendritic integration where inputs are combined linearly across the entire ...