Feng Jiang

• PhD
• Zhongnan University of Economics and Law

Interval prediction is crucial in decision-making processes across many domains. Although significant progress has been made in existing interval prediction methods, they still face several challenges, such as assumptions about data distribution, fixed interval widths, limitations of gradient-based optimization algorithm, crossing of upper and lowe...

Interval prediction of electric load has aroused widespread concern by the power industry because of variability and uncertainty. To quantify the potential uncertainty associated with prediction, this paper proposes a clustering-based approach to construct prediction intervals (PIs) for electric load data. The singular spectrum analysis (SSA) and k...

One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order...

Short-term load forecasting plays a significant role in the management of power plants. In this paper, we propose a multivariate adaptive step fruit fly optimization algorithm (MAFOA) to optimize the smoothing parameter of the generalized regression neural network (GRNN) in the short-term power load forecasting. In addition, due to the substantial...

Computer-aided diagnosis (CAD) can assist doctors with clinical diagnosis and improve diagnosis accuracy and efficiency further. It is significative and valuable for cancer detection by using machine learning. In this paper, a hybrid model based on optimization algorithm and machine learning with feature weighting is carried out to detect breast ca...

Recent advances in experimental biology studies have produced large amount of molecular activity data. In particular, individual patient data provide non-time series information for the molecular activities in disease conditions. The challenge is how to design effective algorithms to infer regulatory networks using the individual patient datasets a...

The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast the concentration of par...

This paper is concerned with quasi-synchronization of stochastic memristor-based neural networks with mixed delays and parameter mismatches. Due to the parameter mismatches, mean-square exponential synchronization generally cannot be achieved directly, then the concept of exponential quasi-synchronization in mean square is introduced. Furthermore,...

In this paper, a novel hybrid learning method is carried out to forecast urban air quality index (AQI). Wavelet packet decomposition (WPD) is firstly performed to decompose the original AQI data into lower-frequency subseries. Then, we improve the pigeon-inspired optimization through using the particle swarm optimization algorithm. The improved pig...

In this paper, a hybrid approach consisting of pigeon-inspired optimization (PIO) and extreme learning machine (ELM) based on wavelet packet analysis (WPA) is presented for predicting bulk commodity futures prices. Firstly, WPA is applied to decompose the original futures prices into a set of lower-frequency subseries. Secondly, the PIO algorithm i...

The reader will have noticed that the proof of Lemma 3.1 in Zhu et al. (2012) is obviously wrong. The same mistake appears in the proof of Theorem 3.1 in Zhu et al. (2015). In this note, we give a corrected proof.

The Ait-Sahalia-Rho model is an important tool to study a number of financial problems, including the term structure of interest rate. However, since the functions of this model do not satisfy the linear growth condition, we cannot study the properties for the solution of this model by using the traditional techniques. In this paper we overcome the...

This paper investigates the stability of second-order stochastic neutral partial functional differential equations driven by impulsive noises. Some sufficient conditions ensuring pth moment exponential stability of the second-order stochastic neutral partial functional differential equations driven by impulsive noises are obtained by establishing a...

In this work, the problem on the exponential stability for second-order neutral stochastic partial differential equations with infinite delays is considered in the presence of impulses under some conditions. By employing the new integral inequality technique, some algebraic criteria of stability are established for the concerned problem and some ex...

The paper is concerned with stability for second-order stochastic neutral partial functional systems subject to infinite delays and impulses. Some sufficient conditions ensuring pth moment exponential stability of the second-order stochastic systems are given by gaining a new integral inequality with impulses. Some earlier results are generalized a...

This paper is concerned with the input-to-state stability problem of a class of neutral stochastic neural networks. The stochastic neural networks that we consider contain both neutral terms and mixed delays. By utilizing the Lyapunov-Krasovskii functional method, stochastic analysis techniques and It 's formula, some sufficient conditions are deri...

This paper introduces a new class of stochastic neural networks with time delays in the leakage terms. A key characteristic of this new stochastic neural network is that its coefficients are dependent on expectations. We first establish a novel stability lemma for this new model. Then, by applying this new stability lemma, Itô's formula, Lyapunov-K...

In this paper, we study the problem of globally asymptotic stabilization for a class of stochastic nonlinear systems. Without requiring the condition that the coefficients are smooth functions, this paper is a first try to apply the backstepping control design method to prove the globally asymptotic stability in probability of stochastic nonlinear...

The advances of systems biology have raised a large number of mathematical models for exploring the dynamic property of biological systems. A challenging issue in mathematical modeling is how to study the influence of parameter variation on system property. Robustness and sensitivity are two major measurements to describe the dynamic property of a...

Semilinear stochastic dynamic systems in a separable Hilbert space often model some evolution phenomena arising in physics and engineering. In this paper, we study the existence and uniqueness of mild solutions to neutral semilinear stochastic functional dynamic systems under local non-Lipschitz conditions on the coefficients by means of the stoppi...

The crack density and crack growth rate are important parameters which are used to describe the fatigue damage and predict fatigue life of a composite material. Even the same good manufacturing practice, the fatigue damage of materials may be different. Also material properties often accompany random fluctuation. Thus stochastic systems are used to...

Recently, hybrid stochastic differential equations have received a great deal of attention. It is surprising that there are not any numerical schemes established for the hybrid stochastic functional differential equations. In this paper, the Euler—Maruyama method is developed, and the main aim is to show that the numerical solutions will converge t...

The purpose of this paper considers a class of mixed implicit quasi-variational inclusion for fuzzy mappings in Hilbert spaces. By using the resolvent operator technique associated with (g, η)-monotone mapping, a new iterative algorithm is given, and an existence and convergence for solving this class of variational inclusions is established and di...

In this paper, we investigate the almost surely asymptotic stability of the nonlinear stochastic pantograph differential equations (SPDEs) with Markovian switching. Linear SPDEs with Markovian switching and nonlinear examples with Markovian switching will be discussed to illustrate the theory.

We introduce a class of mixed nonlinear variational inclusion for fuzzy mappings in Hilbert spaces. By using the resolvent operator technique for maximal monotone mapping, we construct some new iterative algorithms for solving this class of variational inclusions. We prove the existence of solution for this kind of variational inclusions and the co...

This paper is concerned with the mean square stability for stochastic delayed Hopfield neural networks with Markovian switching. The sufficient conditions to guarantee the exponential stability in mean square of an equilibrium solution are given. Moreover, we give the mean square stability of the numerical method. The result shows that the numerica...

Notice of RetractionAfter careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of...