Chaotic time series prediction of E-nose sensor drift in embedded phase space

Zhang, Lei; Tian, Fengchun; Liu, Shouqiong; Dang, Lijun; Peng, Xiongwei; Yin, Xin

doi:10.1016/j.snb.2013.03.003

Cited by 64 publications

(20 citation statements)

References 27 publications

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“…Many methods, such as false nearest neighbor method [20], G-P algorithm [32,33] and Cao's method [34], are proposed to estimate the value of embedding dimension m. In this paper, we only need to obtain the minimum embedding dimension m min based on the Takens embedding theorem [24][25][26][27][28][29]. The G-P algorithm proposed by Grassberger and Procaccia is adopted to determine it due to its easy calculation and good performance.…”

Section: Phase Space Reconstruction (Psr)mentioning

confidence: 99%

“…A new method realized a long-term prediction of sensor baseline and drift based on PSR and RBF neural network. Results show that the proposed model can make long-term and accurate forecasting of chemical sensor baseline and drift time series [20].…”

Section: Introductionmentioning

confidence: 97%

“…PSR in chaos theory can memorize all of the properties of a chaotic attractor and clearly recover the motion trace of a time series, thus PSR provides more information for modeling and makes more accurate forecasting possible [17][18][19][20]. Therefore, we suppose that the WWTP is a chaotic system, but this has not been demonstrated yet.…”

Section: Introductionmentioning

confidence: 99%

“…Chaos is a widely existing phenomenon in nature and is a specific behavior for the nonlinear dynamic system. With the rapid development of chaos theory, the chaotic time series analysis and prediction are widely studied and have been widespread concerned in the research fields such as electric short-term load [17], economics [18], industrial manufacture [19], signal processing [20] and medical diagnosis [21]. A multivariate prediction method was presented for electric short-term load using chaos theory and radial basis function (RBF) neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

Qiao

2016

Water

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Abstract:The precision of soft measurement for biochemical oxygen demand (BOD) is always restricted due to various factors in the wastewater treatment plant (WWTP). To solve this problem, a new soft measurement modeling method based on chaos theory is proposed and is applied to BOD measurement in this paper. Phase space reconstruction (PSR) based on Takens embedding theorem is used to extract more information from the limited datasets of the chaotic system. The WWTP is first testified as a chaotic system by the correlation dimension (D), the largest Lyapunov exponents (λ 1 ), the Kolmogorov entropy (K) of the BOD and other water quality parameters time series. Multivariate chaotic time series modeling method with principal component analysis (PCA) and artificial neural network (ANN) is then adopted to estimate the value of the effluent BOD. Simulation results show that the proposed approach has higher accuracy and better prediction ability than the corresponding modeling approaches not based on chaos theory.

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Section: Phase Space Reconstruction (Psr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

Qiao

2016

Water

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“…Many prediction methods have gained popularity in practice during the last decades based on two main categories. Such as adaptive prediction [17][18][19], the support vector machine (SVM) [20][21][22][23][24][25][26][27], polynomial estimation [28][29][30], and neural network [8][9][10][11][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Local Prediction of Chaotic Time Series Based on Polynomial Coefficient Autoregressive Model

2015

Mathematical Problems in Engineering

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We apply the polynomial function to approximate the functional coefficients of the state-dependent autoregressive model for chaotic time series prediction. We present a novel local nonlinear model called local polynomial coefficient autoregressive prediction (LPP) model based on the phase space reconstruction. The LPP model can effectively fit nonlinear characteristics of chaotic time series with simple structure and have excellent one-step forecasting performance. We have also proposed a kernel LPP (KLPP) model which applies the kernel technique for the LPP model to obtain better multistep forecasting performance. The proposed models are flexible to analyze complex and multivariate nonlinear structures. Both simulated and real data examples are used for illustration.

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Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

Liu,

Sun,

Bao

et al. 2024

Small

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Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom‐thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom‐thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom‐thin materials and ML in fragrance sensing, providing an in‐depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.

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Chaotic time series prediction of E-nose sensor drift in embedded phase space

Cited by 64 publications

References 27 publications

Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

Local Prediction of Chaotic Time Series Based on Polynomial Coefficient Autoregressive Model

Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

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