In this paper, an asynchronous filter is proposed for Markov jump neural networks (NNs) with time delay and quantized measurements where a logarithmic quantizer is employed. The filter and quantizer ...are both mode-dependent and their modes are asynchronous with that of the NN, which is described by hidden Markov models. By the Lyapunov-Krasovskii functional approach, a sufficient condition is derived and a filter is then designed such that the filtering error dynamics are stochastically mean square stable and strictly <inline-formula> <tex-math notation="LaTeX">\boldsymbol {(\mathscr U,\mathscr S,\mathscr V)} </tex-math></inline-formula>-dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.
We propose the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) for model selection in hidden Markov models (HMM) when the number of states is unknown. The exact ...solutions exploit the properties of HMM that allow tractable forms of both criteria to be obtained while transgressing the common assumption in AIC and BIC model selection approaches on the independence of data. The proposed algorithm is presented and evaluated in application to blind channel estimation and symbol detection when the channel length is assumed unknown.
This article discusses the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy asynchronous controller design problem for nonlinear multiagent systems via a dynamic event-triggered scheme in the ...discrete-time context. To formulate the asynchronous phenomena between the system modes and the anticipant controller modes, the hidden Markov model is proposed. The primary attention is focused on the explicit design of the dynamic event-triggered strategy that can be dynamically adjusted in line with system information, which mitigates the communication burden efficiently. On this occasion, the information renewal of the controller is aperiodic. Furthermore, the nonlinear characteristics are effectually disposed through utilizing a unique IT2 T-S fuzzy model, which is with mismatched membership functions (MFs). As a result, the resulting closed-loop fuzzy multiagent systems are accompanied by mismatched MFs and asynchronous modes, whereafter, via solving the convex optimization problem, the desired controller gains are acquired. Eventually, the validity and practicability of the developed control scheme are illustrated by two examples.
In this paper, a novel hidden Markov model (HMM)-driven robust latent variable model (LVM) is proposed for fault classification in dynamic industrial processes. A robust probabilistic model with ...Student's t mixture output is designed for tolerating outliers. Based on the robust LVM, the probabilistic structure is further developed into a classifier form so as to incorporate various types of process information during model acquisition. After that, the robust probabilistic classifier is extended within the HMM framework so as to characterize the time-domain stochastic uncertainties. The model parameters are derived through the expectation-maximization algorithm. For performance validation, the developed model is tested on the Tennessee Eastman benchmark process.
We report the emergence of anomalous (non-Fickian) transport through a rough-walled fracture as a result of increasing normal stress on the fracture. We show that the origin of this anomalous ...transport behavior can be traced to the emergence of a heterogeneous flow field dominated by preferential channels and stagnation zones, as a result of the larger number of contacts in a highly stressed fracture. We show that the velocity distribution determines the late-time scaling of particle spreading, and velocity correlation determines the magnitude of spreading and the transition time from the initial ballistic regime to the asymptotic anomalous behavior. We also propose a spatial Markov model that reproduces the transport behavior at the scale of the entire fracture with only three physical parameters. Our results point to a heretofore unrecognized link between geomechanics and particle transport in fractured media.
•Transport on a rough fracture transitions from Fickian to non-Fickian as confining stress increases.•Confining stress induces self-organization of flow into preferential channels and stagnation regions.•We propose a parsimonious stochastic transport model that captures the transition to anomalous transport.
The integration of a massive number of plug-in electric vehicles (PEVs) into current power distribution networks brings direct challenges to network planning, control, and operation. To increase the ...PEV penetration level with minimal negative impact, the dynamical PEV travel behaviors and charging demand need to be better understood. This paper presents a Markov-based analytical approach for modeling PEV travel behaviors and charging demand. The travel behaviors of individual PEVs are expressed mathematically through Monte Carlo simulation considering two essential factors: temporal travel purposes and state of charge (SoC). Markov model and hidden Markov model (HMM) are adopted to explicitly formulate the probabilistic correlation between multiple PEV states and SoC ranges. This modeling approach provides an efficient and generic tool for analyzing PEV travel behaviors and charging demand based on available PEV statistics. The analytical model is further adopted in the impact assessment of two PEV normal charging scheduling strategies for a range of PEV penetration levels in an IEEE 53-bus test network with field data (network parameters and realistic PEV statistics). The results demonstrate the benefit of the proposed modeling approach in network analysis considering PEV integration.
We present a method for recognizing and localizing actions in video by the sequence of changing appearance and motion of the participants. Appearance is modeled by histogram of oriented gradients ...object detectors, while motion is modeled by optical-flow motion-pattern detectors. Sequencing is modeled by a hidden Markov model (HMM) whose output models are these appearance and motion detectors. The HMM and associated detectors are simultaneously trained, learning the sequence of detectors that match the most distinctive temporal subsequences of the action represented in the training data. Training uses both positive and negative samples of a given action class and is accomplished without the need for annotation of the correspondence between training video frames and the state-conditioned detectors, by minimizing a discriminative cost function through gradient descent. Trained models are used to perform recognition and localization by simultaneous detection, tracking, and action recognition. In contrast to many prior methods, our approach learns intuitively meaningful models that represent action as a sequence of retinotopic models. We demonstrate such by rendering these models on unseen test video. This method was found to perform competitively on three standard datasets, Weizmann, KTH, and UCF Sports, as well as on the video from the Defence Advanced Research Project Agency (DARPA) Mind's Eye program and a newly filmed dataset.
This paper investigates the problem of dissipativity-based asynchronous fuzzy integral sliding mode control (AFISMC) for nonlinear Markov jump systems represented by Takagi-Sugeno (T-S) models, which ...are subject to external noise and matched uncertainties. Since modes of original systems cannot be directly obtained, the hidden Markov model is employed to detect mode information. With the detected mode and the parallel distributed compensation approach, a suitable fuzzy integral sliding surface is devised. Then using Lyapunov function, a sufficient condition for the existence of sliding mode controller gains is developed, which can also ensure the stochastic stability of the sliding mode dynamics with a satisfactory dissipative performance. An AFISMC law is proposed to drive system trajectories into the predetermined sliding mode boundary layer in finite time. For the case with unknown bound of uncertainties, an adaptive AFISMC law is developed as well. The studied T-S fuzzy Markov jump systems involve both continuous-time and discrete-time domains. Finally, some simulation results are presented to demonstrate the applicability and effectiveness of the proposed approaches.
The extensive deployment of wireless infrastructure provides alternative low-cost methods for location awareness of mobile phone users in indoor environments by processing the received signal ...strength (RSS) of the mobile phone. In such a signal processing framework, hidden Markov models (HMMs) are often used to model the uncertainties of RSS data and incorporate environmental information into localization. Since hidden semi-Markov models (HsMMs) outperform HMMs in their ability to model state duration more flexibly, employing HsMMs for indoor user positioning is a promising research direction. In this aspect, a user's personal preference of staying in a particular area, and the functionality of certain areas, such as a dining room, as well as navigation landmarks, can be utilized in the HsMM to assist localization. This paper proposes an online HsMM forward recursion algorithm to incorporate these information for real-time smartphone user tracking. We apply the proposed HsMM forward recursion algorithm to simulated, synthesized, and real RSS datasets in typical indoor environments for validation.
The tool wear monitoring (TWM) system that could estimate tool wear conditions and predict remaining useful life (RUL) is important to meet the high precision requirement and improve productivity in ...automated machining. Due to its good properties in representing nonstationary and complex physical process, hidden semi-Markov Model (HSMM) is adapted to model the progressive tool wear in this paper. In order to describe the time-variant transition probability of tool wear states and the state duration dependency, the HSMM is improved by learning the duration parameters and RUL distribution database. The Forward algorithm is utilized for online tool wear estimation and remaining life prognosis, and an online implementation approach is developed to reduce computational cost. Experimental results show that the approach is effective and the proposed method of duration dependency modeling leads to more accurate TWM in high speed milling.
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