1,097 research outputs found
Max-min Learning of Approximate Weight Matrices from Fuzzy Data
In this article, we study the approximate solutions set of an
inconsistent system of fuzzy relational equations . Using the norm, we compute by an explicit
analytical formula the Chebyshev distance , where is the set of second members of the
consistent systems defined with the same matrix . We study the set
of Chebyshev approximations of the second member i.e.,
vectors such that , which is
associated to the approximate solutions set in the following sense:
an element of the set is a solution vector of a system where . As main results, we
describe both the structure of the set and that of the set
. We then introduce a paradigm for learning weight
matrices that relates input and output data from training data. The learning
error is expressed in terms of the norm. We compute by an explicit
formula the minimal value of the learning error according to the training data.
We give a method to construct weight matrices whose learning error is minimal,
that we call approximate weight matrices.
Finally, as an application of our results, we show how to learn approximately
the rule parameters of a possibilistic rule-based system according to multiple
training data
Postprocesamiento CAM-ROBOTICA orientado al prototipado y mecanizado en células robotizadas complejas
The main interest of this thesis consists of the study and implementation of postprocessors to adapt the toolpath generated by a Computer Aided Manufacturing (CAM) system to a complex robotic workcell of eight joints, devoted to the rapid prototyping of 3D CAD-defined products. It consists of a 6R industrial manipulator mounted on a linear track and synchronized with a rotary table. To accomplish this main objective, previous work is required. Each task carried out entails a methodology, objective and partial results that complement each other, namely:
- It is described the architecture of the workcell in depth, at both displacement and joint-rate levels, for both direct and inverse resolutions. The conditioning of the Jacobian matrix is described as kinetostatic performance index to evaluate the vicinity to singular postures. These ones are analysed from a geometric point of view.
- Prior to any machining, the additional external joints require a calibration done in situ, usually in an industrial environment. A novel Non-contact Planar Constraint Calibration method is developed to estimate the external joints configuration parameters by means of a laser displacement sensor.
- A first control is originally done by means of a fuzzy inference engine at the displacement level, which is integrated within the postprocessor of the CAM software.
- Several Redundancy Resolution Schemes (RRS) at the joint-rate level are compared for the configuration of the postprocessor, dealing not only with the additional joints (intrinsic redundancy) but also with the redundancy due to the symmetry on the milling tool (functional redundancy).
- The use of these schemes is optimized by adjusting two performance criterion vectors related to both singularity avoidance and maintenance of a preferred reference posture, as secondary tasks to be done during the path tracking. Two innovative fuzzy inference engines actively adjust the weight of each joint in these tasks.Andrés De La Esperanza, FJ. (2011). Postprocesamiento CAM-ROBOTICA orientado al prototipado y mecanizado en células robotizadas complejas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10627Palanci
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