A Trend Prediction Method Based on Double Hidden Layer Quantum Circuit Recurrent Unit Neural Network

Abstract

The invention relates to a trend prediction method based on double-hidden-layer quantum circuit cyclic unit neural network, comprising the following steps: constructing a permutation entropy set of original operating data; inputting the permutation entropy set into DHL-QCRUNN for training and prediction, and obtaining a predicted permutation entropy set ;Construct the permutation entropy error set of the predicted value and the actual value at each time point; input the permutation entropy error set into DHL-QCRUNN training and prediction, and obtain the predicted normalized permutation entropy error set; denormalize the processing to obtain the final prediction result. The present invention proposes a new type of quantum neural network—a double-hidden layer quantum circuit recurrent unit neural network. The present invention uses the LM algorithm to update the network parameters of DHL-QCRUNN to improve the convergence performance of the neural network, which is comparable to other artificial intelligence methods. Compared with DHL-QCRUNN, DHL-QCRUNN has better nonlinear approximation ability, generalization characteristics and faster convergence speed. The present invention is used to predict the running trend of the monitored object, and achieves higher prediction accuracy, prediction stability and calculation efficiency.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a trend prediction method based on a double-hidden-layer quantum circuit cyclic unit neural network. Background technique [0002] Rotating machinery (such as engines, steam turbines, etc.), as key equipment widely used in petrochemical, electric power, metallurgy, coal, nuclear energy and other industries, is often affected by various forms of failures. A major accident that resulted in the death of a plane. The application of advanced fault diagnosis technology to rotating machinery can play a key role in ensuring the safe operation of equipment, saving maintenance costs and preventing environmental pollution, and has huge economic benefits. [0003] Generally, the maintenance methods of mechanical equipment can be divided into three types: accident shutdown maintenance, regular shutdown maintenance, and condition maintenance (also known as predictive maintenance). Am...

AI Technical Summary

Problems solved by technology

However, these prediction methods are difficult to obtain ideal prediction results due to their respective defects

For example, the support vector machine kernel function and its parameters are artificially selected in many cases, with many uncertainties; fuzzy logic has similar problems to SVM; in prediction methods based on artificial neural networks, such as BP neural network (Back-Propagation Neural Network) Network, BPNN), recurrent neural network (Recurrent Neural Network, RNN) and other classic neural networks, there are problems such as slow learning convergence speed, difficult training, and unstable learning and memory of the network, while Elman neural network (Elman Neural Network, Elman -NN) and RNN variants such as Long Short Term Memory Neural Network (LSTMNN), due to their own theoretical and structural defects, it is still difficult to make accurate predictions

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