Quantum Particle Swarm Optimization for Elman Recurrent Network

Aziz, Mohamad Firdaus Ab; Shamsuddin, Siti Mariyam

doi:10.1109/ams.2010.39

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Meanwhile, Yu and Wilamowski [17] demonstrated that the used of second order algorithms such as Newton algorithm and Levernberg Marquardt (LM) algorithm also can produced better result which can converge faster than using first order algorithms. Furthermore, Aziz et al [18,19] also produced a very good result of using particle swarm optimization in training Elman neural network.…”

Section: Introductionmentioning

confidence: 99%

An Improved Back Propagation Leaning Algorithm Using Second Order Methods with Gain Parameter

Nawi¹,

Saufi²,

Budiyono³

et al. 2018

IJIE

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Back Propagation (BP) algorithm is one of the oldest learning techniques used by Artificial Neural Networks (ANN). It has successfully been implemented in various practical problems. However, the algorithm still faces some drawbacks such as getting easily stuck at local minima and needs longer time to converge on an acceptable solution. Recently, the introduction of Second Order Methods has shown a significant improvement on the learning in BP but it still has some drawbacks such as slow convergence and complexity. To overcome these limitations, this research proposed a modified approach for BP by introducing the Conjugate Gradient and Quasi-Newton which were Second Order methods together with 'gain' parameter. The performances of the proposed approach is evaluated in terms of lowest number of epochs, lowest CPU time and highest accuracy on five benchmark classification datasets such as Glass, Horse, 7Bit Parity, Indian Liver Patient and Lung Cancer. The results show that the proposed Second Order methods with 'gain' performed better than the BP algorithm.

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Section: Introductionmentioning

confidence: 99%

An Improved Back Propagation Leaning Algorithm Using Second Order Methods with Gain Parameter

Nawi¹,

Saufi²,

Budiyono³

et al. 2018

IJIE

View full text Add to dashboard Cite

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“…The fusion of artificial neural network and quantum theory can better simulate the process of human brain information processing. Quantum neural network can improve the approximation ability and information processing efficiency of artificial neural network, which had been used for many applications [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

Zhang

Gong²

2016

Mathematical Problems in Engineering

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Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means ofK-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

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