Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Finite element method. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Material and design considerations for rapid manufacturing

Abstract: An investigation into how the advent of Rapid Manufacturing will influence an individual designer's approach to product design and materials selection is detailed. The assumption is made that Rapid Prototyping machines have evolved into viable manufacturing systems and all associated problems with accuracy, surface finish and repeatability have been resolved. Without the restriction of removing a product from a tool, designers will be free to design any complexity of geometry that they either desire or require. This tool-less approach to manufacturing will have profound implications on the way designers are accustomed to working and will eliminate most Design for Manufacture considerations. Design investigations are presented to highlight the potential importance and scope of Rapid Manufacturing. With advances in a new range of materials, some Rapid Manufacturing is happening today—albeit with existing Rapid Prototyping equipment. However, to enable designers to use existing Rapid Prototyping equipment fo...

A stable neural network-based observer with application to flexible-joint manipulators

Abstract: A stable neural network (NN)-based observer for general multivariable nonlinear systems is presented in this paper. Unlike most previous neural network observers, the proposed observer uses a nonlinear-in-parameters neural network (NLPNN). Therefore, it can be applied to systems with higher degrees of nonlinearity without any a priori knowledge about system dynamics. The learning rule for the neural network is a novel approach based on the modified backpropagation (BP) algorithm. An e-modification term is added to guarantee robustness of the observer. No strictly positive real (SPR) or any other strong assumption is imposed on the proposed approach. The stability of the recurrent neural network observer is shown by Lyapunov's direct method. Simulation results for a flexible-joint manipulator are presented to demonstrate the enhanced performance achieved by utilizing the proposed neural network observer.