An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis

Chen, Peng; Liu, Yuan; He, Yigang; Luo, Shuai

doi:10.1016/j.neucom.2015.12.131

Cited by 100 publications

(49 citation statements)

References 24 publications

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“…Aminian et al [1] WT + PCA + NN 97 95 Xiao et al [4] FrWT + KPCA + Ridgelet−NN 100 98.52 Yuan et al [9] Entropy + Kurtosis + NN 100 99 Vasan et al [10] WT + entropy, Kurtosis + SVM 99.70 95.69 Song et al [46] FrFT statistical feature + SVM 98.41 95.12 Chen et al [47] WPT As shown in Table 4, it can be observed that our proposed method achieves a better result than that of other listed works, with other exceptions [4,9,10]. However, the fault components in our work have smaller parametric deviation.…”

Section: Workmentioning

confidence: 99%

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Wei

et al. 2018

Entropy

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Abstract:In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits' defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.

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Section: Workmentioning

confidence: 99%

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Wei

et al. 2018

Entropy

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“…Based on the above-mentioned strategy of rotation angle and the step size range (0.005π, 0.1π) given by [24,28], in IDCQGA, the rotation angle function ∆θ is defined as:…”

Section: Adaptive Step Size For Updatingmentioning

confidence: 99%

“…To show the performance of the IDCQGA, an optimization experiment of Shaffer's F6function is designed, and the IDCQGA is compared with PSO [28], GA [20], QGA [26] and conventional DCQGA [29]. Shaffer's F6 can be expressed as:…”

Section: Experiments 1 and Analysis: Performance Of The Idcqgamentioning

confidence: 99%

“…In IDCQGA, the initial rotation angle is not needed because our algorithm is adaptive according to Section 3.2.2. For the PSO algorithm, there are two additional scaling factors c 1 = 1.5 and c 2 = 1.7, which represent the weights of the statistical acceleration that push each particle to the optimum position [28]. The optimization results of the five algorithms for Shaffer's F6 function are shown in Table 2 and Figure 3.…”

Section: Experiments 1 and Analysis: Performance Of The Idcqgamentioning

confidence: 99%

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A Sparse Signal Reconstruction Method Based on Improved Double Chains Quantum Genetic Algorithm

2017

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This paper proposes a novel method of sparse signal reconstruction, which combines the improved double chains quantum genetic algorithm (DCQGA) and the orthogonal matching pursuit algorithm (OMP). Firstly, aiming at the problems of the slow convergence speed and poor robustness of traditional DCQGA, we propose an improved double chains quantum genetic algorithm (IDCQGA). The main innovations contain three aspects: (1) a high density quantum encoding method is presented to reduce the searching space and increase the searching density of the algorithm; (2) the adaptive step size factor is introduced in the chromosome updating, which changes the step size with the gradient of the objective function at the search points; (3) the quantum π/6-gate is proposed in chromosome mutation to overcome the deficiency of the traditional NOT-gate mutation with poor performance to increase the diversity of the population. Secondly, for the problem of the OMP algorithm not being able to reconstruct precisely the effective sparse signal in noisy environments, a fidelity orthogonal matching pursuit (FOMP) algorithm is proposed. Finally, the IDCQGA-based OMP and FOMP algorithms are applied to the sparse signal decomposition, and the simulation results show that the proposed algorithms can improve the convergence speed and reconstruction precision compared with other methods in the experiments.

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“…QGA is a new intelligent search algorithm, which combines GA and quantum information theory to complete global optimization. As a branch of the QGA, double chains quantum genetic algorithm (DCQGA) has been one of the burning research problems in recent years, because of its small population size, strong searching ability and fast convergence speed [24,25]. However, the DCQGA has its own shortcomings: first of all, DCQGA with large encoding space range affects the convergence rate; secondly, the chromosome mutation is treated by the NOT-gate, but it usually cannot achieve the purpose of increasing the population diversity.…”

Section: Introductionmentioning

confidence: 99%

A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA

et al. 2018

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Radar electronic reconnaissance is an important part of modern and future electronic warfare systems and is the primary method to obtain non-cooperative intelligence information. As the task requirement of radar electronic reconnaissance, it is necessary to identify the non-cooperative signals from the mixed signals. However, with the complexity of battlefield electromagnetic environment, the performance of traditional recognition system is seriously affected. In this paper, a new recognition method based on optimal classification atom and improved double chains quantum genetic algorithm (IDCQGA) is researched, optimal classification atom is a new feature for radar signal recognition, IDCQGA with symmetric coding performance can be applied to the global optimization algorithm. The main contributions of this paper are as follows: Firstly, in order to measure the difference of multi-class signals, signal separation degree based on distance criterion is proposed and established according to the inter-class separability and intra-class aggregation of the signals. Then, an IDCQGA is proposed to select the best atom for classification under the constraint of distance criterion, and the inner product of the signal and the best atom for classification is taken as the eigenvector. Finally, the extreme learning machine (ELM) is introduced as classifier to complete the recognition of signals. Simulation results show that the proposed method can improve the recognition rate of multi-class signals and has better processing ability for overlapping eigenvector parameters.

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An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis

Cited by 100 publications

References 24 publications

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

A Sparse Signal Reconstruction Method Based on Improved Double Chains Quantum Genetic Algorithm

A New Radar Signal Recognition Method Based on Optimal Classification Atom and IDCQGA

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