Huadong Li-Gong Daxue xuebao

Neural Network Algorithm and Application of the ECG Classification

Yang Yan, YU Jin-shou

Published Jan 1, 2009• 0 citations

Abstract

By introducing quantum superposition into neural networks,this paper proposes a model of quantum neural network,in which the units in the hidden and output layers adopt the superposition of multi-level activation functions to obtain the multi-lever partitions of the feature space.During the training,the units can be `collapsed-in' and `spread-out' according to practical requirement.Moreover,this proposed algorithm can learn the inaccuracy and uncertainties from the input of fuzzy information.It is shown from the application for ECG classification that the proposed algorithm has faster training speed and better performance.

Related papers

A new training algorithm for Quantum Neural Networks

Jian Sun, Pla Uni

Inspired by the principle of superposing states in quantum mechanics,a new kind of feedforward neural network with single hidden layer was designed and named as Quantum Neural Networks.It has mainly been used for data classification.Multilevel activation functions is adopted in Quantum Neural Networks and a different objective function is used for the learning of quantum intervals of the multilevel hidden units;minimizing the class-conditional variances at the outputs of the hidden units.Quantum Neural Networks is endowed with a special ability of identifying uncertainty in data classification by its unique design.However,as different objective functions are used for the learning of synaptic weights and quantum intervals,conflict is observed in the iterative training process which will cause more iteration times for training and degrades the training results.Using the theory of Constrained Optimization,a new training algorithm is proposed in this paper by introducing penalty function into the steepest descent method.In the new training algorithm, the conflict is removed.Experimental results indicate that the new algorithm can increase the training speed and improve the performance of the trained network remarkably.

Signal ProcessingJan 1, 20111 citation

A novel quantum neural network based on multi-level activation function

Xu-Feng Niu, Wenping Ma

Abstract Since the multi-level activation function quantum neural network (QNN) for pattern recognition was firstly proposed by Purushothaman and Karayiannis, more and more researches have been conducted on improving it. However, they all ignore that the QNN only uses multi-level activation function to simulate the concept of quantum superposition rather than really using quantum computing. In this paper, we propose a real QNN model based on multi-layer activation function. In addition, we present algorithms for updating weight parameters and quantum intervals, and also improve the learning algorithm for weight parameters using famous Levenberg–Marquardt algorithm. We also use the QNN for lie detection, and the simulation results of MATLAB prove that the performance of the model based on the corresponding algorithm are very strong.

Laser Physics LettersJan 7, 202116 citations

Algorithm and Application of the Quantum-Inspired Neural Network Model

Yang Shu-yu

To enhance the approximation and generalization ability of classical artificial neural networks,a quantum-inspired neural network model,whose input of each dimension is a discrete sequence,is proposed.This model concludes three layers,in which the hidden layer consists of quantum-inspired neurons,and the output layer consists of common neurons.The quantum-inspired neuron consists of the quantum rotation gates and the multi-qubits controlled-rotation gates.By using the information feedback of target qubit from output to input in multi-qubits controlled-rotation gate,the overall memory of input sequences is realized.The output of quantum-inspired neuron is obtained from the entanglements of multi-qubits in controlled-rotation gates.The learning algorithm is designed in detail according to the basic principles of quantum computation.The characteristics of input sequence can be effectively obtained by way ofbreadthanddepth.The simulation results show that,when the input nodes and the length of the sequence satisfy a certain relations,the proposed model is obviously superior to the common artificial neural networks.

Dianzi xuebaoJan 1, 20140 citations

Quantum fuzzy neural network based on fuzzy number

Xin Yang

Neural network is one of the AI algorithms commonly used to process data, and has an extremely important position in scenarios such as image recognition, classification, and machine translation. With the increase of data volume explosion, the required computing power of neural networks is also significantly increased. The emergence of quantum neural networks improves the computational power of neural networks, but the accuracy of neural networks and quantum neural networks is not high in the face of the complexity and uncertainty of big data. In order to improve the efficiency and accuracy, the cross-fusion of "fuzzy number theory + quantum neural network" is proposed to study the quantum fuzzy neural network (FQNN) based on fuzzy number. The Gaussian fuzzy function is used to generate the corresponding fuzzy affiliation matrix to describe the uncertain information in the data. The fuzzy independent variables are trained through the FQNN model, and the model is output after changing the parameters of the quantum forward propagation layer. Simulation experiments show that the quantum fuzzy neural network model based on fuzzy number is more efficient and accurate in this study compared with the quantum neural network model.

Frontiers in Computing and Intelligent SystemsApr 13, 20232 citations

Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks

Gopathy Purushothaman, Nicolaos B. Karayiannis

This paper introduces quantum neural networks (QNNs), a class of feedforward neural networks (FFNNs) inherently capable of estimating the structure of a feature space in the form of fuzzy sets. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Unlike other approaches attempting to merge fuzzy logic and neural networks, QNNs can be used in pattern classification problems without any restricting assumptions such as the availability of a priori knowledge or desired membership profile, convexity of classes, a limited number of classes, etc. Experimental results presented here show that QNNs are capable of recognizing structures in data, a property that conventional FFNNs with sigmoidal hidden units lack.

IEEE Transactions on Neural NetworksMay 1, 1997163 citations

Data Classification using Quantum Neural Network

Ghassan H. Al- Majeed, Zainab T. Alisa, Hassan Saadallah Naji

In this paper, integrated quantum neural network (QNN), which is a class of feedforward neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that (QNN’s) are capable of recognizing structures in data, a property that conventional (FFNN’s) with sigmoidal hidden units lack. In addition, (QNN) gave a kind of fast and realistic results compared with the (FFNN). Simulation results indicate that QNN is superior (with total accuracy of 97.778%) than ANN (with total accuracy of 93.334%).

Journal of EngineeringJun 19, 20230 citations