Predicting worsted spinning performance with an artificial neural network model

For a given fiber spun to pre-determined yarn specifications, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills, and then generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for predicting worsted spinning performance with an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for predicting spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan YarnspecTM). The ANN is then subsequently trained with commercial mill data to assess the feasibility of the method as a mill-specific performance prediction tool. Incorporating mill-specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately. <br /

Plastic Representation of the Reachable Space for a Humanoid Robot

Reaching a target object requires accurate estimation of the object spatial position and its further transformation into a suitable arm-motor command. In this paper, we propose a framework that provides a robot with a capacity to represent its reachable space in an adaptive way. The location of the target is represented implicitly by both the gaze direction and the angles of arm joints. Two paired neural networks are used to compute the direct and inverse transformations between the arm position and the head position. These networks allow reaching the target either through a ballistic movement or through visually-guided actions. Thanks to the latter skill, the robot can adapt its sensorimotor transformations so as to reflect changes in its body configuration. The proposed framework was implemented on the NAO humanoid robot, and our experimental results provide evidences for its adaptative capabilities

On Soft Learning Vector Quantization Based on Reformulation

The complex admissibility conditions for reformulated function in Karayiannis model is obtained based on the three axioms of radial basis function neural network. In this paper, we present an easier understandable assumption about vector quantization and radial basis function neural network. Under this assumption, we have obtained a simple but equivalent criterion for admissible reformulation function in Karayiannis model. We have also discovered that Karayiannis model for vector quantization has a trivial fixed point. Such results are useful for developing new vector quantization algorithms.Computer Science, Artificial IntelligenceComputer Science, Theory &amp; MethodsSCI(E)CPCI-S(ISTP)