Jian Xiao

A novel chaotic particle swarm optimization based fuzzy clustering algorithm

Clustering is a popular data analysis and data mining technique. In this paper, a novel chaotic particle swarm fuzzy clustering (CPSFC) algorithm based on chaotic particle swarm (CPSO) and gradient method is proposed. Fuzzy clustering model optimization is challenging, in order to solve this problem, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC) are introduced, and a new CPSO algorithm combined AIWF and ICMIC based chaotic local search is studied. The CPSFC algorithm utilizes CPSO to search the fuzzy clustering model, exploiting the searching capability of fuzzy c-means (FCM) and avoiding its major limitation of getting stuck at locally optimal values. Meanwhile, gradient operator is adopted to accelerate convergence of the proposed algorithm. Its superiority over the FCM algorithm and another two global optimization algorithm-based clustering methods is extensively demonstrated for several artificial and real life data sets in comparative experiments.

Hydraulic turbine governing system identification using T-S fuzzy model optimized by chaotic gravitational search algorithm

Hydraulic turbine governing system (HTGS) is a complicated nonlinear system that controls the frequency and power output of hydroelectric generating unit (HGU). The modeling of HTGS is an important and difficult task, because some components, like hydraulic turbine and governor actuator, are with strong nonlinearity. In this paper, a novel Takagi-Sugeno (T-S) fuzzy model identification method based on chaotic gravitational search algorithm (CGSA) is proposed and applied in the modeling of HTGS. In the proposed method, fuzzy c-regression model clustering algorithm is used to partition the input space and identify the coarse antecedent membership function (MF) parameters at first. And then, a novel CGSA is proposed to search better MF parameters around the coarse results, in which chaotic search has been embedded in the iteration of basic GSA to search and replace the current best solution of GSA. The performance of the proposed fuzzy model identification method is validated by benchmark problems, and the results show that the accuracies of identified models have been improved significantly compared with the other existing models. Finally, the proposed approach has been applied to approximate the dynamic behaviors of HTGS of a HGU in a hydropower station of Jiangxi Province of China. The experimental results show that our approach can identify the HTGS satisfactorily with acceptable accuracy.

Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings

Rolling element bearings (REB) are crucial mechanical parts of most rotary machineries, and REB failures often cause terrible accidents and serious economic losses. Therefore, REB fault diagnosis is very important for ensuring the safe operation of rotary machineries. In previous researches on REB fault diagnosis, achieving the accurate description of faults has always been a difficult problem, which seriously restricts the reliability and accuracy of the diagnosis results. In order to improve the precision of fault description and provide strong basis for fault diagnosis, dependent feature vector (DFV) is proposed to denote the fault symptom attributes of the six REB faults in this paper, and this is a self-adaptive fault representation method which describes each fault sample according to its own characteristics. Because of its unique feature selection technique and particular structural property, DFV is excellent in fault description, and could lay a good foundation for fault diagnosis. The advantages of DFV are theoretically proved via the Euclidean distance evaluation technique. Finally, a fault diagnosis method combining DFV and probability neural network (PNN) is proposed and applied to 708 REB fault samples. The experimental results indicate that the proposed method can achieve an efficient accuracy in REB fault diagnosis.

New criteria for exponential stability of delayed recurrent neural networks

In this paper, we investigate exponential stability of delayed recurrent neural networks. By using the delay partitioning method, some sufficient conditions are established to guarantee exponential stability of delayed recurrent neural networks under two different conditions with constructing new Lyapunov-Krasvoskii functional. This partitioning approach can reduce the conservatism comparing with some previous results of stability. At last, numerical examples are given out to show the effectiveness and advantage of our results.

Global robust stability of uncertain delayed neural networks with discontinuous neuron activation

This paper integrates global robust stability of uncertain delay neural networks with discontinuous activation. The activation function is unbounded and the uncertainties are norm bound. By the homotopy invariance and solution properties of the topological degree, the conditions for the existence of equilibrium are given out. Moreover, based on the Lyapunov–Krasovskii stability theory, the conditions of global robust stability for discontinuous delayed neural networks with uncertainties are presented in terms of linear matrix inequality. At last, an illustrative numerical example is provided to show the effectiveness of results given.

Letters: Global asymptotic stability of delayed neural networks with discontinuous neuron activations

In this paper, we integrate a class of delayed neural networks with discontinuous activations, which are not supposed to be bounded or nondecreasing. Conditions of existence of an equilibrium point are established by means of the Leray-Schauder theorem of set-valued maps. Then, the existence of solutions is proved based on viability theorem. Furthermore, global asymptotical stability of the networks is studied by using Lyapunov-Krasovskii stability theory. The results of global asymptotical stability are in term of linear matrix inequality. The obtained results extend previous works on global stability of delayed neural networks with discontinues activations.

Dynamic evolution evoked by external inputs in memristor-based wavelet neural networks with different memductance functions

In this paper, we present a preliminary study concerning the dynamic flows in memristor-based wavelet neural networks with continuous feedback functions and discontinuous feedback functions in the presence of different memductance functions. The theoretical studies as well as the computer simulations confirm our claim. The analysis can characterize the fundamental electrical properties of memristor devices and provide convenience for applications.

Passivity Analysis of Delayed Neural Networks with Discontinuous Activations

In this paper, we investigate the passivity problem of delayed neural networks, where the activation functions are discontinuous. Based on differential inclusion theory, sufficient conditions for this problem are obtained by means of generalized Lyapunov approach. The theoretical results can be checked by solving some linear matrix inequalities. The results extend previous researches on the passivity of delayed neural networks.

Robust Exponential Stabilization of Uncertain Complex Switched Networks with Time-Varying Delays

This paper investigates the robust stability for a class of uncertain complex switched networks (CSN) with time-varying delays and switching topology. The CSN model contains switching behaviors on both nodes and the topology configuration which is general in many complex networks. Based on Lyapunov stability theory and the comparison principle, sufficient robust exponential stabilization conditions for CSN are established via the impulsive control schemes under two different conditions. The corresponding systematic-design procedure is presented, and a numerical example is provided to illustrate the effectiveness of our methods.

Fault diagnosis based on comprehensive geometric characteristic and probability neural network

Abstract Fault diagnosis is very important to ensure the safe operation of hydraulic generator units (HGU). Shaft orbit identification has been highlighted as an effective method for HGU fault diagnosis in the past few years. The purpose of this paper is to propose a novel shaft orbit identification method based on comprehensive geometric characteristics and probability neural network (CGC–PNN) for HGU fault diagnosis. In this method, macroscopic Euler-number (ME), fuzzy convex–concave feature (FCC) and boundary-layer feature (BL) are proposed to represent shaft orbits from three different aspects: structure, region and boundary. Therefore, the most effective and comprehensive image information is fully integrated by the feature vector composed of ME, FCC and BL. Furthermore, probability neural network (PNN) has been introduced as the classifier according to the simplicity of the feature vector. Finally, we apply the proposed method to 800 samples and the experimental results indicate that the proposed method can achieve an efficient accuracy in HGU fault diagnosis.