Neurons in the visual cortex of monkeys respond selectively to the disparity between the images in the two eyes. Recent recordings have shown that some of the disparity-selective neurons in the primary visual cortex and the posterior parietal cortex are modulated by the distance of fixation. A population of such gain-modulated, disparity-selective neurons forms a set of basis functions of horizontal disparity and distance of fixation that can be used as an intermediate representation for computing egocentric distance. This distributed representation is consistent with psychophysical studies of human depth perception; in contrast, neurons explicitly tuned to distance are not consistent with how we perceive distance. In a population model that includes noise in the firing rates of neurons, the perceived distance is

A previous neural network algorithm for multipath mitigation on GPS-based attitude determination is adapted to space applications, where the attitude is unknown in the training process. One considers a user spacecraft with at least three GPS antennas and attitude determined from single differences of the GPS carrier phase L1. A recipe inspired on the on-board calibration problem for star sensors is used to cope with the lack of observability that arises when attitude is unknown. The algorithm was tested with simulated data based on a ground experiment and was able to reduce the multipath effect in more than 50%.

Abstract. Many efforts have been made to develop general dynamical models of the Van Allen radiation belts based on data alone. Early linear prediction filter studies focused on the response of daily-averaged relativistic electrons at geostationary altitudes(5; 1). Vassiliadis et al (2005)(10) extended this technique spatially by incorporating SAMPEX electron flux data into linear prediction filters for a broad range of L-shells from 1.1 to 10.0 RE. Nonlinear state space models(6) have provided useful initial results on the timescales involved in modelling the impulse-reponse of the radiation belts. Here, we show how NARMAX models, in conjunction with nonlinear time-delay FIR neural networks (Volterra networks) hold great promise for the development of accurate and fully dataderived space weather specification and forecast tools. 1. Theory The overall methodology we have adopted is based on using NARMAX models to then contruct equivalent nonlinear Volterra neural networks that benefit from Takens ’ Theorem for time-delay embedding combined with the ability of

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Abstract The purpose of this paper is to design an adaptive controller and system experimental implementation for nonlinear translational oscillations with a rotational actuator (TORA) system. A wavelet-based neural network (WNN) is proposed to develop an adaptive backstepping control scheme, called ABCWNN for TORA system. To ensure the stability of the controlled system, a compensated controller is designed to enhance the control performance. Based on its universal approximation ability, we use a WNN to estimate the system uncertainty including frictional forces, external disturbance, and parameter variance. According to the estimations of the WNNs, the ABCWNN control is developed via a backstepping design procedure such that the system outputs follow the desired trajectories. For system development, the effects of frictional forces are discussed and solved using the estimation of the WNN. The effectiveness of the proposed control scheme for TORA system is verified by numerical simulation and experimental results.