Parameter estimation for multirate multi-input systems using auxiliary model and multi-innovation

The multirate multi-input systems have different updating periods and sampling periods such that the conventional identification algorithms cannot be used to identify such multirate systems. By using the auxiliary model identification idea, the multiinnovation stochastic gradient algorithm is developed to estimate the parameters of multirate systems. Finally, an illustrative example is given to verify the effectiveness of the proposed algorithm.

FLOATING QUANTIZATION EFFECTS ON MULTIRATE SAMPLED-DATA NONLINEAR CONTROL SYSTEMS

In this article, floating quantization effects on multirate sampled-data control systems are studied. It shows that the solutions of multirate digital feedback control systems with nonlinear plant and with floating quantization in the controller are uniformly ultimately bounded if the associated linear systems consisting of linearization of the plant and controller with no quantization are Schur stable. Moreover, it also shows that the difference between the response of multirate digital controllers without quantizers and the same plant with floating quantization in the controllers can be made as small as desired by selecting proper quantization level.

Throughput Analysis of IEEE 802.11 Multirate WLANs with Collision Aware Rate Adaptation Algorithm

This paper presents a mathematical model that analyzes the throughput of the IEEE 802.11b distributed coordination function （DCF） with the collision aware rate adaptation （CARA） algorithm. IEEE 802.11 WLANs provide multiple transmission rates to improve system throughput by adapting the transmission rate to the current channel conditions. The system throughput is determined by some stations using low transmission rates due to bad channel conditions. CARA algorithm does not disturb the existing IEEE 802.11b formats and it can be easily incorporated into the commercial wireless local area networks （WLAN） devices. Finally, we verify our findings with simulation.

Outlier Rejecting Multirate Model for State Estimation

Wavelet transform was introduced to detect and eliminate outliers in time-frequency domain. The outlier rejection and multirate information extraction were initially incorporated by wavelet transform, a new outlier rejecting multirate model for state estimation was proposed. The model is applied to state estimation with interacting multiple model, as the outlier is eliminated and more reasonable multirate information is extracted, the estimation accuracy is greatly enhanced. The simulation results prove that the new model is robust to outliers and the estimation performance is significantly improved.

Overview of multirate sampled-data control system

The basic analysis and synthesis approaches for multirate sampled-data control system are reviewed. After giving the definition and some properties of multirate system are given, its origination, development and design methods are discussed in detail. Finally, some remarks, expectations and conclusions on the present research status and the research directions are given.

Joint Optimization of Admission Control and Rate Adaptation for Video Sharing over Multirate Wireless Community Cloud

Emerging wireless community cloud enables usergenerated video content to be shared and consumed in a social context. However, the nature of shared wireless medium and timevarying channels seriously limits the quality of service(QoS), partially owing to the lack of mechanisms for effectively utilizing multi-rate channel resources. In this paper, the joint optimization of admission control and rate adaptation is proposed, resulting in a bandwidth-aware rate-adaptive admission control(BRAC) scheme to provide bandwidth guarantee for sharing social multimedia contents. The analytical approach leads to the following major contributions:(1) a bandwidth-aware rate selection(BRS) algorithm to optimally meet the bandwidth requirement of the data session and channel conditions at the physical layer;(2) a routing-coupled rate adaption and admission control algorithm to admit data sessions with bandwidth guarantee. Moreover, extensive numerical simulations suggest that BRAC is efficient and effective in meeting the bandwidth requirements for sharing social multimedia contents. These insights will shed light on communication system implementation for multimedia content sharing over multirate wireless community cloud.

Multirate diversity strategy of fractal modulation

Previous analyses of fractal modulation were carried out mostly from a signle perspective or a subband, but the analyses from the perspective of multiscale synthesis have not been found yet; while multiscale synthesis is just the essence of the mutlirate diversity which is the most important characteristic of fractal modulation. As for the mutlirate diversity of fractal modulation, previous studies only dealt with the general outspread of its concept, lacked the thorough and intensive quantitative comparison and analysis. In light of the above fact, from the perspective of multiscale synthesis, in this paper we provide a comprehensive analysis of the multirate diversity of fractal modulation and corresponding quantitative analysis. The results show that mutlirate diversity, which is a fusion of frequency diversity and time diversity, pays an acceptable price in spectral efficiency in exchange for a significant improvement in bit error rate. It makes fractal modulation particularly suitable for the channels whose bandwidth and duration parameters axe unknown or cannot be predicted to the transmitter. Surely it is clearly of great significance for reliable communications. Moreover, we also attain the ability to flexibly make various rate-bandwidth tradeoffs between the transmitter and the receiver, to freely select the reception time and to expediently control the total bandwidth. Furthermore, the acquisitions or improvements of these fine features could provide support of the technical feasibility for the electromagnetic spectrum control technology in a complex electromagnetic environment.

Fuzzy modeling of multirate sampled nonlinear systems based on multi-model method

Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied.Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.

A MULTIRATE PROCESSING APPROACH FOR MICROWAVE IMAGING

In this paper,a multirate processing approach for microwave imaging is presented.This approach has the advantages of largely compressing the raw spectral data for imaging,greatly reducing the storage requirement and enhancing the processing efficiency.To demonstrateits applicability,the proposed approach is tested on both simulated and experimental data.

Transfer Function Model of Multirate Feedback Control Systems

Based on the suitably defined multivariable version of Kranc operators and the extended input and output vectors, the multirate sampling plant is transformed to a equivalent time invariant single rate one, then the transfer function model of the multivariable multirate sampling plant is obtained. By combining this plant model with the time invariant description of the multirate controller in terms of extended vectors, the closed loop transfer function model of the multirate feedback control system can be determinated. This transfer function model has a very simple structure, and can be used as a basis for the analysis and synthesis of the multirate sampling feedback control systems in the frequency domain.

Robust Pole Assignment of Digital Control System with Output Multirate Sampled

Multirate digital control system is a periodically time-variant (PTV) system in its essence. It bas many' super capability', such as obtaining arbitrarily-large gain- margin, simultaneous stabilization, strong stabilization, decentralized control, etc. Utilizing freedom aroused from the multirate sampling of system output, this paper assigns poles of the closedloop system robustly, and so improves the resistance of the system to perturbation.

Transfer Function Description of Multirate Sampling Systems

Based on the sampler decomposition method and modified Z transform, this paper proposes a pulse transfer function matrix description of the multivariable multirate sampling systems. This multirate sampling system model has a simple structure, and can be used as a basis for the analysis and synthesis of the multirate sampling systems.

Chip Interleaved Multirate and Multimedia Transmission in Self-Encoded Communication System with Iterative Detection over Fading Channels

In this paper, we investigate the self-encoded multirate and the multimedia (SEMM) transmission. In SEMM system, the spreading codes are derived from its own previously transmitted bits rather than the pseudorandom code generator. We employ block chip interleaving at the transmitter to combat the deep fading over the channels. At the receiver, decorrelation scheme separates the combined signals not only to reduce the crosstalk between different applications, but also provide a better estimation for the despreading sequence. Interference cancelation (IC) is also adopted to improve both the correlation detection and iterative detection (ID) performance. The simulation results show that the proposed scheme significantly improves performance over fading channels.

Performance of Multirate Multicast in Distributed Network

The number of Internet users has increased very rapidly due to the scalability of the network. The users demand for higher bandwidth and better throughput in the case of on demand video, or video conference or any real time distributed network system. Performance is a great issue in any distributed networks. In this paper we have shown the performance of the multicast groups or clusters in the distributed network system. In this paper we have shown the performance of different users or receivers belongs to the multicast group or cluster in the distributed network, transfer data from the source node with multirate multicast or unirate multicast by considering packet level forwarding procedure in different sessions. In this work we have shown that how the throughput was effected when the number of receiver increase. In this work we have considered the different types of queue such as RED, Fair queue at the junction node for maintaining the end to end packet transmission. In this work we have used various congestion control protocol at the sender nodes. This paper we have shown the performance of the distributed cluster network by multirate multicast.

MULTIRATE TIME ITERATIVE SCHEME WITH MULTIPHYSICS FINITE ELEMENT METHOD FOR A NONLINEAR POROELASTICITY

In this paper,a multirate time iterative scheme with multiphysics finite element method is proposed and analyzed for the nonlinear poroelasticity model.The original problem is reformulated into a generalized nonlinear Stokes problem coupled with a diffusion problem of a pseudo pressure field by a new multiphysics approach.A multiphysics finite element method is adopted for the spatial discretization,and the generalized nonlinear Stokes problem is solved in a coarse time step and the diffusion problem is solved in a finer time step.The proposed algorithm is a decoupled algorithm,which is easily implemented in computation and reduces greatly computation cost.The stability analysis and the convergence analysis for the multirate iterative scheme with multiphysics finite element method are given.Some numerical tests are shown to demonstrate and validate the analysis results.

Energy efficiency and savings analysis with multirate sampling for petrochemical process using convolutional neural network-based transfer learning

Energy efficiency in the petrochemical industry is crucial in reducing energy consumption and environmental impact.An accurate energy efficiency model will provide valuable insight for supporting operational adjustment decisions.In practice,due to inconsistent sampling intervals in the petrochemical industry,the traditional approach for obtaining energy efficiency may be unreliable and difficult to handle these multirate data char-acteristics.Therefore,in this paper,a multi-channel convolutional neural network model integrating a model parameter-based transfer learning approach is proposed to improve the prediction of energy efficiency under inconsistent sampling intervals.The multi-channel structure aims to recognize a different pattern from the dataset by convolving the information along the time dimension.Concurrently,transfer learning allows the model to learn a new pattern of input after the model is fully trained.Finally,the performance for energy ef-ficiency prediction and saving analysis is validated by applying it to the vinyl chloride monomer production case study.The result shows that the proposed model outperformed traditional models and typical convolutional neural network structures in terms of accuracy and reproducibility,with an r-square of 0.97.The utilization of transfer learning prevents a significant drop in performance and enhances adaptability in model learning on real-time energy tracking.Moreover,the energy gap analysis of the prediction result identified a significant energysaving potential,which would decrease annual energy consumption by 7.25%on average and a 5,709-ton reduction in carbon dioxide emissions.

Receding horizon estimation to networked control systems with multirate scheme

This paper investigates the problem of receding horizon state estimation for networked control systems （NCSs） with random network-induced delays less than one sample period, which are formulated as multirate control systems. Based on a batch of recent past slow rate measurements in a finite horizon window, the initial state estimation in this window is solved by minimizing a receding-horizon objective function, and then the fast rate state estimations are calculated by the prediction of dynamic equation to compensate for the network-induced time delays. Furthermore, convergence results and unbiasedness properties are analyzed. An upper bound of estimation error is presented under the assumption of bounded disturbances acting on the system and measurement equations. A simulation example shows the effectiveness of the proposed method.

Observer-based Multirate Feedback Control Design for Two-time-scale System

The use of a lower sampling rate for designing a discrete-time state feedback-based controller fails to capture information of fast states in a two-time-scale system, while the use of a higher sampling rate increases the amount of computation considerably. Thus,the use of single-rate sampling for systems with slow and fast states has evident limitations. In this paper, multirate state feedback(MRSF) control for a linear time-invariant two-time-scale system is proposed. Here, multirate sampling refers to the sampling of slow and fast states at different sampling rates. Firstly, a block-triangular form of the original continuous two-time-scale system is constructed. Then, it is discretized with a smaller sampling period and feedback control is designed for the fast subsystem. Later, the system is block-diagonalized and equivalently represented into a system with a higher sampling period. Subsequently, feedback control is designed for the slow subsystem and overall MRSF control is derived. It is proved that the derived MRSF control stabilizes the full-order system. Being the transformed states of the original system, slow and fast states need to be estimated for the MRSF control realization.Hence, a sequential two-stage observer is formulated to estimate these states. Finally, the applicability of the design method is demonstrated with a numerical example and simulation results are compared with the single-rate sampling method. It is found that the proposed MRSF control and observer designs reduce computations without compromising closed-loop performance.

Controller Optimization for Multirate Systems Based on Reinforcement Learning

The goal of this paper is to design a model-free optimal controller for the multirate system based on reinforcement learning.Sampled-data control systems are widely used in the industrial production process and multirate sampling has attracted much attention in the study of the sampled-data control theory.In this paper,we assume the sampling periods for state variables are different from periods for system inputs.Under this condition,we can obtain an equivalent discrete-time system using the lifting technique.Then,we provide an algorithm to solve the linear quadratic regulator(LQR)control problem of multirate systems with the utilization of matrix substitutions.Based on a reinforcement learning method,we use online policy iteration and off-policy algorithms to optimize the controller for multirate systems.By using the least squares method,we convert the off-policy algorithm into a model-free reinforcement learning algorithm,which only requires the input and output data of the system.Finally,we use an example to illustrate the applicability and efficiency of the model-free algorithm above mentioned.

Generalized Yule-walker and two-stage identification algorithms for dual-rate systems

In this paper, two approaches are developed for directly identifying single-rate models of dual-rate stochastic systems in which the input updating frequency is an integer multiple of the output sampling frequency. The first is the generalized Yule-Walker algorithm and the second is a two-stage algorithm based on the correlation technique. The basic idea is to directly identify the parameters of underlying single-rate models instead of the lifted models of dual-rate systems from the dual-rate input-output data, assuming that the measurement data are stationary and ergodic. An example is given.