The emerging technology of quantum neural networks (QNNs) attracts great attention from both the fields machine learning and physics with capability to gain advantage an artificial network (ANN) system. Comparing classical counterparts, QNNs have been proven be able speed up information processing, enhance prediction or classification efficiency as well offer versatile experimentally friendly p...

This paper describes an application of Artificial Neural Networks (ANN) to Load Frequency Control (LFC) of nonlinear power systems. Power systems, such as other industrial processes, have parametric uncertainties that for controller design had to take the uncertainties in to account. For this reason, in the design of LFC controller the idea of robust control theories are being used. To improve ...

This paper a new method for the control of Two linkrobotic manipulator systems using Neural Network has been presented, The first method is based on Proportional-Derivative controller, The second method is based on Proportional-IntegralDerivative controller, the third method is based on artificial Neural Network by PD controller, and the forth method is based on artificial Neural Network by PID...

In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. Then, an attitude control based on feedback linearization control is designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. In order to eliminate the effect of these uncertainties, ...

This paper discusses a class issue of finite-time lag projective synchronization (FTLPS) delayed fractional-order quaternion-valued neural networks (FOQVNNs) with parameter uncertainties, which is solved by non-decomposition method. Firstly, new FOQVNNs model uncertain parameters designed. Secondly, two types feedback controller and adaptive without sign functions are designed in the quaternion...

In this paper, a quaternion-based finite-time sliding mode attitude controller is designed for a spacecraft performing high-thrust orbital maneuvers, with cold gas thrusters as its actuators. The proposed controller results are compared with those of a quaternion feedback controller developed for the linearized spacecraft dynamics, in terms of settling time, steady-state error, number of thrust...

Running Deep Neural Network (DNN) models on devices with limited computational capability is a challenge due to large compute and memory requirements. Quantized Neural Networks (QNNs) have emerged as a potential solution to this problem, promising to offer most of the DNN accuracy benefits with much lower computational cost. However, harvesting these benefits on existing mobile CPUs is a challe...