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The maneuver control of hypersonic vehicles (HSVs) during re-entry is a challenging work due to the object features of serious nonlinearity, strong uncertainty and fast time variation. In this paper, we design the maneuver control architecture of lateral turning for the HSV first, and then a new self-organizing recurrent functional link network (SORFLN) is proposed to estimate the dynamical uncertainties/disturbances...
In this paper, we present a reinforcement learning model of the shepherding of a flock of sheep by a dog. The shepherding task, a heuristic model originally proposed by Strombom, et al., describes the dynamics of the sheep while being herded by a dog to a predefined target. This study recreates the proposed model using SARSA, an algorithm for learning the optimal policy in reinforcement learning....
Artificial Swarm Intelligence (ASI) strives to facilitate the emergence of a super-human intellect by connecting groups of human users in closed-loop systems modeled after biological swarms. Prior studies have shown that “human swarms” can make more accurate predictions than traditional methods for tapping the wisdom of groups, such as votes and polls. To further test the predictive ability of swarms,...
This paper develops an adaptive control scheme for position and velocity tracking control of high speed trains under uncertain system nonlinearities and actuator failures. Neural networks with self-organizing capabilities are integrated into control design, where the number of the neurons can be adjusted online automatically, so as not only to avoid the problem inherent in the NN with fixed structure...
This paper presents a modification of reference-model adaptive control with a layered network of higher-order neural units (HONUs) as adaptive state-feedback controllers. The degree of freedom of such neural controller is deemed here as the number of applied HONUs of a customizable polynomial order and as of their individually customizable input vectors. Furthermore, the control scheme is enhanced...
We present a novel neural episodic memory architecture that utilizes reservoir computing to extract and recall information gleaned over time from a multilayer perceptron that receives sensory input. Reservoir computing models project input data into a high-dimensional dynamical space and also serve as a fading memory that holds on to past inputs thereby enabling the direct association of the current...
This paper proposes an obstacle avoidance method for navigation of a mobile robot in uncertain environments based on a novel neural learning algorithm, namely least mean p-norm extreme learning machine (LMP-ELM) and Q-learning. The proposed obstacle avoidance method comprises of two behavior modules, Viz., an avoidance behavior and goal-seeking behavior. At the learning phase, the two modules are...
Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented...
In the recent times, offshore activities are getting increasingly important, and marine vessels are prevalent in all the water bodies. This requires a detailed study of the effect of environmental forces of the marine structures. This paper aims at developing an unified framework to study the effect of wind force and moments on marine vessels. A neural network approach is developed to study the effect...
Capturing large diffeomorphic deformations is difficult for many non-rigid registration methods. In this paper, we propose Log-Demons with driving force for large deformation image registration. The driving force obtained by boundary points correspondence exerts influence on continuous optimization of Log-Demons to improve the motion direction of points. We utilize MROGH descriptor matching to obtain...
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