Retrieval of the Atmospheric Temperature and Humidity Profiles Using a Feed-Forward Neural Network

Using a feed-forward neural network, an inverse algorithm was developed to profile the vertical structure of temperature and specific humidity. The inverse algorithm (inverse model) was used to calculate temperature and humidity profiles, which were then compared with other existing methods. The inverse model is found efficient in profiling the vertical structure of temperature and humidity as compared to other existing methods. For example, the statistical methods notorious for their high computational cost, altitude-dependent error, and inability to accurately retrieve the vertical temperature and humidity profiles, are enhanced with an inverse model. The inverse model’s diurnal and seasonal cycle profiles are also found superior to those of other existing methods, which could be useful for assimilation in numerical weather forecast models. We suggest that incorporating such an inverse model into the ground-based microwave radiometer (GMWR) will enhance the accuracy of the vertical structure of temperature and humidity profiles, and so the improvement in weather forecasting. The developed inverse model has a resolution of 50 m between the surface to 500 m and 100 m between 500–2000 m, and 500 m beyond 2000 m.

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A Feed Forward Neural Network for Solving the Inverse Kinetics of Non-Constant Curvature Soft Manipulators Driven by Cables

Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control ◽

10.1115/dscc2013-3740 ◽

2013 ◽

Cited By ~ 2

Author(s):

Michele Giorelli ◽

Federico Renda ◽

Gabriele Ferri ◽

Cecilia Laschi

Keyword(s):

Neural Network ◽

Computational Cost ◽

Iterative Algorithms ◽

Average Error ◽

Feed Forward Neural Network ◽

Feed Forward ◽

Neural Network Learning ◽

The Neural Network ◽

Tip Position ◽

Kinetics Of

The solution of the inverse kinetics problem of soft manipulators is essential to generate paths in the task space to perform grasping operations. To address this issue, researchers have proposed different iterative methods based on Jacobian matrix. However, although these methods guarantee a good degree of accuracy, they suffer from singularities, long-term convergence, parametric uncertainties and high computational cost. To overcome intrinsic problems of iterative algorithms, we propose here a neural network learning of the inverse kinetics of a soft manipulator. To our best knowledge, this represents the first attempt in this direction. A preliminary work on the feasibility of the neural network solution has been proposed for a conical shape manipulator driven by cables. After the training, a feed-forward neural network (FNN) is able to represent the relation between the manipulator tip position and the forces applied to the cables. The results show that a desired tip position can be achieved quickly with a degree of accuracy of 0.73% relative average error with respect to total length of arm.

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