Being one of the largest social media platforms, Twitter has a diverse community collaborating on multitude ideas. With large amounts data being generated and collected every day, it is perfect platform to implement machine learning algorithms analyze information in different tweets. A recurrent Neural Network (RNN) specific algorithm that used solve problems involving sequential such as texts ...

The grip force required to handle an object depends on the object’s mass and friction coefficient of its surface. control in myoelectric prosthesis is crucial for handling objects adequately. In current paper we propose a new method improving proportional continuous grasping estimation improve systems based surface electromyography (sEMG) recordings. For this purpose, develop approach multivari...

This paper presents two recurrent neural networks for solving the assignment problem. Simplifying the architecture of a recurrent neural network based on the primal assignment problem, the first recurrent neural network, called the primal assignment network, has less complex connectivity than its predecessor. The second recurrent neural network, called the dual assignment network, based on the ...

Presents a recurrent neural network for solving the Sylvester equation with time-varying coefficient matrices. The recurrent neural network with implicit dynamics is deliberately developed in the way that its trajectory is guaranteed to converge exponentially to the time-varying solution of a given Sylvester equation. Theoretical results of convergence and sensitivity analysis are presented to ...

A new trainable and recurrent neural optimization algorithm, which has potentially superior capabilities compared to existing neural search algorithms to compute high quality solutions of static optimization problems in a computationally efficient manner, is studied. Specifically, local stability analysis of the dynamics of a relaxation-based recurrent neural network, the Simultaneous Recurrent...

Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neural networks implies storing them as different attractors of the recurrent hidden layer dynamics. A challenging test and also useful for application is conditional prediction of sequences giving just the trigger signal as an input and letting the recurrent neural ne...

A new model order and time-delay selection method for neural network modelling of SISO non-linear systems has been recently proposed. The extension of this method to the MIMO case is developed in this paper. The MIMO form of the NARX model is considered and the order and time-delay for each input are selected by identifying linearised models of the system. Application of the method to a simulat...

in this paper, we consider the problem of efficient computation of the forward kinematics of a 6 DOF robot manipulator built to use in rehabilitation purpose. Forward kinematics problem (FKP) of parallel robots is very difficult to solve in comparison to the serial manipulators. This problem is almost impossible to solve analytically. Numerical methods are one of the common solutions for this p...