Nonstationary Multiwavelets and Multiwavelet Packets in Sobolev Space $$H^s({\mathbb {R}}^d)$$

Kumar, Raj; Chauhan, M. S.

doi:10.1007/s40819-020-00889-0

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Cubic zirconia is an important semiconductor and crystalline material with high processing accuracy, high oxygen ion conductivity and other physicochemical properties leading to a wide range of applications in the field of optoelectronics, such as fuel cells, oxygen sensors and fibre optic connectors. [1][2][3][4][5] In nature, zirconia exists in the monoclinic phase. Zirconia undergoes a phase transition from monoclinic to tetragonal at 1170 °C and from tetragonal to cubic at 2370 °C.…”

Section: Introductionmentioning

confidence: 99%

Local structure regulation and spectroscopy study of blue cubic zirconia

et al. 2023

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Section: Introductionmentioning

confidence: 99%

Local structure regulation and spectroscopy study of blue cubic zirconia

et al. 2023

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“…Scholars have done a lot of research work on electrocardiogram recognition. Übeyli et al [20] proposed a recurrent neural network (RNN) to classify heart beat types. Xiong et al [21] classified the features through threelayer BP neural network, which had a high automatic detection and recognition rate in ventricular premature beats.…”

Section: Introductionmentioning

confidence: 99%

Chaotic neural networks with complex-valued weight and its applications in electrocardiogram classification

Zhang,

Chen,

Kou

et al. 2023

Phys. Scr.

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As neural networks are easy to converge to local minimum, the ergodicity of chaotic system is helpful to tackle this problem. Besides, the real parts and imaginary parts in complex-valued chaotic systems are independent, which increase the ergodic property and unpredictability of the chaotic signals. Therefore, we propose a new chaotic neural network with complex-valued weight for electrocardiogram classification. Firstly, a complex Logistic chaotic map is put forward, and its bifurcation diagram, Lyapunov index, and chaotic attractors are analyzed. Secondly, based on the ergodicity of complex Logistic chaotic map and a novel neuron function, the learning algorithm including complex-valued weight iteration for the chaotic neural network is proposed. Finally, the MIT-BIH data-base is used to verify the proposed method. The chaotic neural network with real Logistic map and other classification methods are also adopted for comparison. The results show that our chaotic neural network has a certain improvement in the accuracy of electrocardiogram classification.

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Nonstationary Multiwavelets and Multiwavelet Packets in Sobolev Space $$H^s({\mathbb {R}}^d)$$

Cited by 2 publications

References 30 publications

Local structure regulation and spectroscopy study of blue cubic zirconia

Local structure regulation and spectroscopy study of blue cubic zirconia

Chaotic neural networks with complex-valued weight and its applications in electrocardiogram classification

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