This paper presents the Application of Quaternion Neural Network (QNN) to Electromyography (EMG) based Estimation of Forearm Motion. Motion of human body can be modeled as a set of rotations in three-dimensional space by various joints. The aim of this research is to show the efficiency of QNN in estimation from EMG. We are trying to learn, estimate and simulate combined motion using QNN as wel...

Using hydraulic interconnected suspension (HIS) system to improve the stability of the vehicles is a matter of recent interest of many scholars. In this paper, application of this kind of suspension system and its impact on the stability of the vehicle are studied. The governing dynamic relations of the system are presented, using free body diagram, Newton-Euler motion equations, and relations ...

Quantum Neural Network (QNN) can improve upon the inadequacies of the classical neural network (CNN). The CNN requires a huge memory and needs more computational power. A new field of computation is emerging which integrates quantum computation with CNN. A quantum inspired hybrid model of quantum neurons and classical neurons is proposed. This paper details an approach, perhaps the first attemp...

Conventional quaternion based methods have been extensively employed for spacecraft attitude control where the aerodynamic forces can be neglected. In the presence of aerodynamic forces, the flight attitude control is more complicated due to aerodynamic moments and inertia uncertainties. In this paper, a robust nero-adaptive quat...

Premature ventricular contractions (PVCs) are ectopic heart beats originating from ventricular area. It is a common form of heart arrhythmia. Electrocardiogram (ECG) recordings have been widely used to assist cardiologists to diagnose the problem. In this paper, we study the automatic detection of PVC using a fuzzy artificial neural network named Quantum Neural Network (QNN). With the quantum n...

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the pro...

This paper presents competent Method for Optimizing Artificial Neural Network Using Quantum Based Algorithm. In the evolutionary process, the Quantum bits refined so that the probability of finding the optimal network is increased. The probability representation reduced the non positive impact of the permutation problem and the risk of the potential network. To finds near-optimal connection wei...

In this paper, rough sets (RS) and quantum neural network (QNN) are used to recognize electrocardiogram (ECG) signals. Firstly, wavelet transform (WT) is used as a feature extraction after normalization of these signals. Then the attribute reduction of RS has been applied as preprocessor so that we could delete redundant attributes and conflicting objects from decision making table but remain e...

The goal of the artificial neural network is to create powerful artificial problem solving systems. The field of quantum computation applies ideas from quantum mechanics to the study of computation and has made interesting progress. Quantum Neural Network (QNN) is one of the new paradigms built upon the combination of classical neural computation and quantum computation. It is argued that the s...

It is suggested that a quantum neural network (QNN), a type of artificial neural network, can be built using the principles of quantum information processing. The input and output qubits in the QNN can be implemented by optical modes with different polarization, the weights of the QNN can be implemented by optical beam splitters and phase shifters. Since it was first proposed by Feynman [1], th...