Binary-valued Output Research Articles

Distributed system identification for linear stochastic systems with binary sensors

The problem of distributed identification of linear stochastic system with unknown coefficient θ∗ over time-varying networks is considered. For estimating θ∗, each agent in the network can only access the input and the binary-valued output of the local system. Compared with the existing works on distributed optimization and estimation, the binary-valued local output observation considered in the paper makes the problem challenging. By assuming that the agent in the network can communicate with its adjacent neighbors, a stochastic approximation-based distributed identification algorithm is proposed, and the consensus and convergence of the estimates are established. Finally, a numerical example is given showing that the simulation results are consistent with the theoretical analysis.

System identification with binary-valued output observations under either-or communication and data packet dropout

The event-triggered mechanism can effectively save communication resources. However, if the packet loss occurs during data transmission, the receiving center is unable to distinguish between “no data has been sent since the event is not triggered” and “the data has been sent, but the packet is lost”. This paper aims to address this issue in the system identification with binary-valued output observations under either-or communication and data packet dropout. For the case that the packet loss probability is known, the identification algorithm is designed, and the strong convergence and asymptotic normality of the algorithm are given. For the case that the packet loss probability is unknown, a compensation-oriented identification approach is proposed by improving the either-or communication mechanism. The identification algorithm is designed to jointly estimate the system parameter and the packet loss probability, then its convergence and asymptotic normality are derived. The minimum communication rate is given within the allowable range of estimation error and the implementation of the optimal communication mechanism is provided. The validity and rationality of the results are verified by numerical simulation.

Channel Identification of Non-linear Systems with Binary-Valued Output Observations Based on Positive Definite Kernels

Nowadays, the kernel methods are increasingly developed, they are a significant source of advances, not only in terms of computational cost but also in terms of the obtained efficiencies in solving complex tasks, they are founded on the theory of reproducing kernel Hilbert spaces (RKHS). In this paper, we propose an algorithm for recursive identification of finite impulse response (FIR) nonlinear systems, whose outputs are detected by binary value sensors. This algorithm is based on a nonlinear transformation of the data using a kernel function. This transformation performs a basic change that allows the data to be projected into a new space where the relationships between the variables are linear. To test the accuracy of the proposed algorithm, we have compared it with another algorithm proposed in the literature, for that, we employ the practical frequency selective fading channel, called Broadband Radio Access Network (BRAN). Monte Carlo simulation results, in noisy environment and for various data length, demonstrate that the proposed algorithm can give better precision.

Prediction-based event-triggered identification of quantized input FIR systems with quantized output observations

This paper addresses the identification of finite impulse response (FIR) systems withboth quantized and event-triggered observations.An event-triggered communication scheme for the binary-valued output quantization is introducedto save communication resources.Combining the empirical-measure-based identification techniqueand the weighted least-squares optimization,an algorithm is proposed to estimate the unknown parameterby full use of the received data and thenot-triggered condition.Under quantized inputs,it is shown that the estimate can strongly converge to the real valuesand the estimator is asymptotically efficient in terms of the Cramer-Rao lowerbound.Further, the limit of the average communication rate is derived and the tradeoff between this limitand the estimation performance is discussed.Moreover, the case of multi-threshold quantized observations is considered.Numerical examples are included to illustrate the obtained main results.

An approach for fault prediction in SOA-based systems using machine learning techniques

PurposeSoftware fault prediction is an important concept that can be applied at an early stage of the software life cycle. Effective prediction of faults may improve the reliability and testability of software systems. As service-oriented architecture (SOA)-based systems become more and more complex, the interaction between participating services increases frequently. The component services may generate enormous reports and fault information. Although considerable research has stressed on developing fault-proneness prediction models in service-oriented systems (SOS) using machine learning (ML) techniques, there has been little work on assessing how effective the source code metrics are for fault prediction. The paper aims to discuss this issue.Design/methodology/approachIn this paper, the authors have proposed a fault prediction framework to investigate fault prediction in SOS using metrics of web services. The effectiveness of the model has been explored by applying six ML techniques, namely, Naïve Bayes, Artificial Networks (ANN), Adaptive Boosting (AdaBoost), decision tree, Random Forests and Support Vector Machine (SVM), along with five feature selection techniques to extract the essential metrics. The authors have explored accuracy, precision, recall, f-measure and receiver operating characteristic curves of the area under curve values as performance measures.FindingsThe experimental results show that the proposed system can classify the fault-proneness of web services, whether the service is faulty or non-faulty, as a binary-valued output automatically and effectively.Research limitations/implicationsOne possible threat to internal validity in the study is the unknown effects of undiscovered faults. Specifically, the authors have injected possible faults into the classes using Java C3.0 tool and only fixed faults are injected into the classes. However, considering the Java C3.0 community of development, testing and use, the authors can generalize that the undiscovered faults should be few and have less impact on the results presented in this study, and that the results may be limited to the investigated complexity metrics and the used ML techniques.Originality/valueIn the literature, only few studies have been observed to directly concentrate on metrics-based fault-proneness prediction of SOS using ML techniques. However, most of the contributions are regarding the fault prediction of the general systems rather than SOS. A majority of them have considered reliability, changeability, maintainability using a logging/history-based approach and mathematical modeling rather than fault prediction in SOS using metrics. Thus, the authors have extended the above contributions further by applying supervised ML techniques over web services metrics and measured their capability by employing fault injection methods.

FIR system identification under either–or communication with quantised inputs and quantised output observations

This study investigates the quantised-input FIR (finite impulse response) system identification under quantised and event-triggered output observations. For the binary-valued quantisation, they introduce a so-called either-or communication scheme to save communication resources. Based on the received data and the triggered indicator, an identification algorithm is proposed to estimate the unknown parameter by use of the empirical measure and weighted least squares. The convergence performance is established and the communication rate is derived. For the multi-threshold quantisation, it can be seen as a vector-valued observation in which each component represents a binary-valued output, and the corresponding identification problem is discussed. A numerical example is included to illustrate the main results obtained.

Asymptotically efficient non-truncated identification for FIR systems with binary-valued outputs

Asymptotically efficient non-truncated identification for FIR systems with binary-valued outputs

Event-triggered identification of FIR systems with binary-valued output observations

This paper investigates the identification of FIR (finite impulse response) systems whose output observations are subject to both the binary-valued quantization and the event-triggered scheme. Based on the a priori information of the unknown parameters and the statistical property of the system noise, a recursive stochastic-approximation-type identification algorithm is proposed. Under a class of persistently exciting inputs, the algorithm is proved to be strongly convergent and the convergence rate of the estimation error is also established, where the corresponding event-triggering conditions are provided. Moreover, the communication rate is discussed. A numerical example is included to verify the effectiveness of the results obtained.

Recursive identification of systems with binary-valued outputs and with ARMA noises

This paper considers the identification problem of the ARMA system followed by a binary sensor, in which the internal variables are corrupted by additive ARMA noises. Recursive estimates for the parameters of the linear system and for the threshold of the binary sensor are given by the stochastic approximation algorithms with expanding truncations (SAAWET). Under reasonable conditions, all the estimates are proved to be convergent to the true values with probability one. The almost surely convergence rates are also investigated. A simulation example is included to justify the theoretical results.

Parameter estimation for systems with structural uncertainties based on quantised inputs and binary‐valued output observations

This study investigates the parameter estimation with general quantised inputs and binary-valued output observations for systems with structural uncertainties of the time-dependent bias and the non-linear model mismatch. Combining the empirical-measure-based technique and the least-squares optimisation, an identification algorithm is proposed by use of the output threshold information and the distribution function of the system noise. It is shown that the estimate of the unknown parameter can be sandwiched in between two constructed auxiliary sequences, whose limits are just a lower bound of the limit inferior of the algorithm and an upper bound of the limit superior, respectively. Moreover, probabilistic estimation error bounds are given. Numerical simulations are presented to illustrate the main theoretical results.

Recursive Nonparametric Identification of Nonlinear Systems With Adaptive Binary Sensors

In this paper, the problem of identifying nonlinear systems under adaptive binary-valued output measurements is considered. We follow a nonparametric approach, which directly estimates the value of the nonlinear function representing the system at any fixed point with the help of a recursive kernel-based stochastic approximation algorithm with expanding truncations (SAAWET). The thresholds of the binary sensor are adaptively designed to achieve a sufficient richness of information in the output observations. The constructed estimates are proved to converge to the true values with probability one. Two numerical examples are given showing that the simulation results are consistent with the theoretical analysis.

Identification of Wiener systems with quantized inputs and binary-valued output observations

This paper investigates identification of Wiener systems with quantized inputs and binary-valued output observations. By parameterizing the static nonlinear function and incorporating both linear and nonlinear parts, we begin by investigating system identifiability under the input and output constraints. Then a three-step algorithm is proposed to estimate the unknown parameters by using the empirical measure, input persistent patterns, and information on noise statistics. Convergence properties of the algorithm, including strong convergence and mean-square convergence rate, are established. Furthermore, by selecting a suitable transformation matrix, the asymptotic efficiency of the algorithm is proved in terms of the Cramér–Rao lower bound. Finally, numerical simulations are presented to illustrate the main results of this paper.

Recursive identification of FIR systems with binary-valued outputs and communication channels

This paper investigates the recursive identification of the finite impulse response systems whose outputs are subject to both the binary-valued quantization and the communication unreliability. The discrete memoryless channel with packet losses and the additive white Gaussian noise channel with time-delay are considered, a stochastic gradient type algorithm and a stochastic approximation type algorithm are respectively proposed to estimate the unknown system parameters. Under some mild conditions, the algorithms can be proved to be convergent in the almost sure and mean square senses. Also, the convergence rates are given. Numerical examples are included to illustrate the performance of the algorithms.

Implementation of Trinary/Quaternary Addition using Multivalve Logic Digital Circuit

of multivalve logic design is to reduce number of gates needed and also to reduce interconnect path length. Interconnect path consist of the largest number of gates from input to output. The reason of these two objectives is that they will give extremely good properties when implemented in VLSI. Reducing number of gates will reduce the chip area, and minimizing interconnect path length will give opportunity to use highest clock frequency. In this paper quaternary to binary and binary to quaternary converter are designed. We can design the multivalve logic to binary converter which is use for conversion of ternary-valued input 0,1,2 and quaternary- valued input 0,1,2,3 into corresponding binary-valued output 0,1. The physical design of the circuits is simulated and tested with MICROWIND layout design tool in 50nm technology. The conversion method is simple and compatible with the present CMOS process. The circuits could be embedded in digital CMOS VLSI design architectures.

Signal estimation with binary-valued sensors

This paper introduces several algorithms for signal estimation using binary-valued output sensing. The main idea is derived from the empirical measure approach for quantized identification, which has been shown to be convergent and asymptotically efficient when the unknown parameters are constants. Signal estimation under binary-valued observations must take into consideration of time varying variables. Typical empirical measure based algorithms are modified with exponential weighting and threshold adaptation to accommodate time-varying natures of the signals. Without any information on signal generators, the authors establish estimation algorithms, interaction between noise reduction by averaging and signal tracking, convergence rates, and asymptotic efficiency. A threshold adaptation algorithm is introduced. Its convergence and convergence rates are analyzed by using the ODE method for stochastic approximation problems.

State reconstruction for linear time-invariant systems with binary-valued output observations

This paper focuses on state reconstruction problems for systems whose outputs are measured only by binary-valued sensors. A binary-valued sensor is characterized by its switching threshold or post-switching location and by its output that indicates only whether the system output is above or below the threshold. The traditional passive-type state reconstruction that does not require input design will fail in general, even if the system has a full-rank observability matrix. It is shown that under controllability conditions and bounded uncertainty on the initial state, it is always possible to construct a causal input that will cause the output to cross the sensor threshold in a designated time interval. Information on the time instants of threshold crossing is then used to reconstruct the initial state. It is proved that if the system is observable, such a reconstruction is always possible if the eigenvalues of the system are all real valued. If the eigenvalues of the system contain only purely imaginary and non-repeating values, it is sufficient that threshold crossing occurs within a relatively small time interval. In general without constraints on system eigenvalues, an input can always be randomized to ensure that the state can be reconstructed with probability one. These results lead to an active state reconstruction algorithm.

Identification Input Design for Consistent Parameter Estimation of Linear Systems With Binary-Valued Output Observations

Input design is of essential importance in system identification for providing sufficient probing capabilities to guarantee convergence of parameter estimates to their true values. This paper presents conditions on input signals that characterize their probing richness for strongly consistent parameter estimation of linear systems with binary-valued output observations. Necessary and sufficient conditions on periodic signals are derived for sufficient richness. These conditions are further studied under different system configurations including open-loop and feedback systems, and different scenarios of noises including actuator noise, input measurement noise, and output measurement noise. In addition to system parameter estimation, essential properties of identifiability and input conditions are also derived when sensor thresholds or noise distribution functions are unknown. The findings of this paper provide a foundation to study identification of systems that either use binary-valued or quantized sensors or involve communication channels, which mandate quantization of signals.

Identification of Wiener systems with binary-valued output observations

This work is concerned with identification of Wiener systems whose outputs are measured by binary-valued sensors. The system consists of a linear FIR (finite impulse response) subsystem of known order, followed by a nonlinear function with a known parametrization structure. The parameters of both linear and nonlinear parts are unknown. Input design, identification algorithms, and their essential properties are presented under the assumptions that the distribution function of the noise is known and the nonlinearity is continuous and invertible. It is shown that under scaled periodic inputs, identification of Wiener systems can be decomposed into a finite number of core identification problems. The concept of joint identifiability of the core problem is introduced to capture the essential conditions under which the Wiener system can be identified with binary-valued observations. Under scaled full-rank conditions and joint identifiability, a strongly convergent algorithm is constructed. The algorithm is shown to be asymptotically efficient for the core identification problem, hence achieving asymptotic optimality in its convergence rate. For computational simplicity, recursive algorithms are also developed.

Joint identification of plant rational models and noise distribution functions using binary-valued observations

System identification of plants with binary-valued output observations is of importance in understanding modeling capability and limitations for systems with limited sensor information, establishing relationships between communication resource limitations and identification complexity, and studying sensor networks. This paper resolves two issues arising in such system identification problems. First, regression structures for identifying a rational model contain non-smooth nonlinearities, leading to a difficult nonlinear filtering problem. By introducing a two-step identification procedure that employs periodic signals, empirical measures, and identifiability features, rational models can be identified without resorting to complicated nonlinear searching algorithms. Second, by formulating a joint identification problem, we are able to accommodate scenarios in which noise distribution functions are unknown. Convergence of parameter estimates is established. Recursive algorithms for joint identification and their key properties are further developed.

RATIONAL MODEL IDENTIFICATION WITH UNKNOWN NOISE DISTRIBUTION USING BINARY DATA

This paper introduces new methods in system identification with binary-valued output observations. It resolves two key issues, (a) regression structures for identifying a rational model that contain non-smooth nonlinearity, and (b) unknown noise distributions arising in practical identification problems. Optimal identification errors, time complexity, and input design are examined in a stochastic information framework.