Binary-valued Observations Research Articles

Recursive Projected Filter Algorithm with Binary-Valued Observations

Recursive Projected Filter Algorithm with Binary-Valued Observations

Detection of data tampering attack in FIR system identification with binary-valued observations

This paper addresses the security detection of data tampering attack in identification of finite impulse response systems with quantized observations. First, we designed a detection algorithm for data tampering attack based on the prior information of the system and gave its online detection form. Second, the introduction of the concepts of the false judgment rate and the missed judgment rate gave a specific evaluation index for the performance of the detection algorithm. Considering the high nonlinearity of binary quantized and the difficulty of obtaining distribution function, we provided calculation methods for two indexes and gave expressions under large samples. Then, the effects of the data length, the prior information of the system parameter, and the attack strategy on the detection algorithm are discussed in this paper. Finally, the theoretical results are verified by numerical simulation.

FIR system identification under congruential summation-triggered communication scheme with binary observations

With the development of information technology, the amount of data transmitted in the network is getting larger and larger, and how to effectively utilize the communication resources has become the focus of a lot of research. In the framework of Finite Impulse Response (FIR) systems, this paper proposes a congruential summation-triggered communication scheme based on the cumulative effect of binary-valued observations to address the parameter identification problem, and its communication rate is derived. Then, the parameter estimation algorithm is designed under periodic input, and its strong convergence is proved, while an index is established to characterize the convergence performance of the algorithm. Furthermore, the trade-off problem between algorithm performance and communication resource is modeled as an optimization problem with constraints, and its explicit solution is given. Finally, numerical simulations are performed to verify the correctness and reasonableness of the algorithm.

Optimal Period Input Design in FIR System Identification With Binary-Valued Observations and Event-Triggered Communication

In the identification of networked systems, the sender uses the event-triggered mechanism to reduce the times of sending data to save communication resources, but the receiver needs as much data as possible to improve the estimation accuracy. This paper tries to mediate the contradiction between the two by means of input design. First, based on the communication rate and the convergence rate of the identification algorithm, the problem of optimal input design is became an optimization problem with constraints. Then, the algorithm for solving this optimization problem is given. Next, the two-time-scale implementation method of optimal input is proposed, where the updated time of the parameter estimation constitutes the identified time scale, and the input is updated based on the estimated value of the system parameter and lasts for a period of time that constitutes the duration time scale of the input. Finally, the validity of the algorithm is verified by numerical simulation.

Asymptotic consensus of multi-agent systems under binary-valued observations and observation uncertainty

Asymptotic consensus of multi-agent systems under binary-valued observations and observation uncertainty

Identification of FIR Systems With Binary-Valued Observations Against Data Tampering Attacks

This article addresses the security issue against data tampering attacks in the identification of finite impulse response (FIR) systems with binary-valued observations. First, the data tampering rate and estimation error caused by the network attack are derived. From the perspective of the attacker, it is investigated how to achieve the maximum attack effect with the minimum energy. Second, the compensation-type defense scheme is designed. Under this, the identification algorithm is constructed, and its strong convergence is proved. Taking the covariance matrix of the estimation error as the performance index, the optimal defense scheme is given. Finally, the results obtained are verified by simulation example.

Estimation of IIR Systems with Binary-Valued Observations

Estimation of IIR Systems with Binary-Valued Observations

Identification of FIR systems with binary-valued observations against denial-of-service attacks

DoS (Denial-of-Service) attack is a very common network attack, under which it is of great practical significance to study the system performance. This paper addresses the security problem in the identification of FIR (Finite Impulse Response) systems with binary-valued observations when the data is under DoS attacks in the process of transmission. From the attacker’s point of view, the optimal attack strategy to maximize the estimation error is given, where the attack is constrained by the average and the maximum energies. From the defender’s point of view, the encryption-type defense scheme is proposed, and the sufficient and necessary conditions are given to make the algorithm consistent. The effectiveness of the conclusion is verified by numerical simulation. Finally, the correctness of the obtained conclusion is verified by numerical simulation.

Adaptive Regulation of Block-Oriented Nonlinear Systems Using Binary Sensors With Applications to Automotive Engine Control

In this article, adaptive regulation of block-oriented nonlinear systems, i.e., Hammerstein and Wiener systems, with binary-valued measurements of the regulation errors is considered. Compared with the classical framework for stochastic adaptive control, the new feature here is that only binary-valued observations of regulation errors are available to the controller. An adaptive regulator based on the stochastic approximation algorithm is proposed and it is proved that the regulator is optimal in the sense that it minimizes the long-run average of the squared regulation errors almost surely. Numerical examples as well as real applications of the proposed algorithms to automotive engine control are given.

Either-or communication-based identification of FIR systems with binary-valued observations and channel uncertainty

Event-triggered mechanism can effectively save communication resources, however, when it encounters channel uncertainty, the remote receiver cannot distinguish between “the sender did not send data” and “the sender sent data but the data lost” when it does not receive data, which causes that it is difficult to make full use of the information provided by the event-triggered mechanism. This paper addresses the identification of FIR (Finite Impulse Response) systems with binary-valued observations and either-or communication mechanism when the packet loss probability is known and unknown respectively. When the packet loss probability is known, it is used for compensation in the parameter estimation. An online identification algorithm is proposed, its strong convergence is proved, and its asymptotic normality is given. Furthermore, how does the packet loss probability affect the algorithm performance is discussed. When the packet loss probability is unknown, an identification algorithm is proposed to jointly estimate it and unknown system parameters by redesigning the either-or communication mechanism. The strong convergence of the algorithm is shown. The tradeoff between the communication rate and the convergence performance of the identification algorithm is modelled as a constrained optimization problem, and its solution is obtained. The rationality of theoretical results is verified by numerical simulation.

Identification of ARMA models with binary-valued observations

This paper studies system identification of ARMA models with binary-valued observations. Compared with existing quantized identification of ARMA models, this problem is more challenging since the accessible information is much less. Different from the identification of FIR models with binary-valued observations, the prediction of original system output and the parameter both need to be estimated in ARMA models. We propose an online identification algorithm consisting of parameter estimation and prediction of original system output. The parameter estimation and the prediction of original output are strongly coupled but mutually reinforcing. By analyzing the two estimates at the same time instead of analyzing separately, we finally prove that the parameter estimate can converge to the true parameter with convergence rate O(1/k) under certain conditions. Simulations are given to demonstrate the theoretical results.

Identification and adaptation with binary-valued observations under non-persistent excitation condition

Dynamical systems with binary-valued observations are widely used in information industry, technology of biological pharmacy and other fields. Though there have been much efforts devoted to the identification of such systems, most of the previous investigations are based on first-order gradient algorithm which usually has much slower convergence rate than the Quasi-Newton algorithm. Moreover, persistence of excitation (PE) conditions are usually required to guarantee consistent parameter estimates in the existing literature, which are hard to be verified or guaranteed for feedback control systems. In this paper, we propose an online projected Quasi-Newton type algorithm for parameter estimation of stochastic regression models with binary-valued observations and varying thresholds. By using both the stochastic Lyapunov function and martingale estimation methods, we establish the strong consistency of the estimation algorithm and provide the convergence rate, under a signal condition which is considerably weaker than the traditional PE condition and coincides with the weakest possible excitation known for the classical least square algorithm of stochastic regression models. Convergence of adaptive predictors and their applications in adaptive control are also discussed.

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks: the optimality of attack strategy

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks: the optimality of attack strategy

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks: defense scheme and its optimality

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks: defense scheme and its optimality

Distributed Recursive Projection Identification with Binary-Valued Observations

This paper investigates a distributed recursive projection identification problem with binary-valued observations built on a sensor network, where each sensor in the sensor network measures partial information of the unknown parameter only, but each sensor is allowed to communicate with its neighbors. A distributed recursive projection algorithm is proposed based on a specific projection operator and a diffusion strategy. The authors establish the upper bound of the accumulated regrets of the adaptive predictor without any requirement of excitation conditions. Moreover, the convergence of the algorithm is given under the bounded cooperative excitation condition, which is more general than the previously imposed independence or persistent excitations on the system regressors and maybe the weakest one under binary observations. A numerical example is supplied to demonstrate the theoretical results and the cooperative effect of the sensors, which shows that the whole network can still fulfill the estimation task through exchanging information between sensors even if any individual sensor cannot.

Suboptimal adaptive tracking control for FIR systems with binary-valued observations

Suboptimal adaptive tracking control for FIR systems with binary-valued observations

Asymptotically Efficient Recursive Identification of FIR Systems With Binary-Valued Observations

This paper considers the identification problem of finite impulse response (FIR) systems with binary-valued observations under the assumption of fixed threshold and bounded persistently excitations. A recursive projection algorithm is constructed to estimate the unknown parameter. For first-order FIR systems, the convergence properties of the algorithm are analyzed theoretically. With mild conditions on the weight coefficients in the parameter update, the algorithm is proved to be convergent in mean square and the convergence rate can be the reciprocal of the number of observations, which has the same order as the optimal estimation when the system output is exactly known. Furthermore, it is also shown that the Cramer–Rao (CR) lower bound is achieved asymptotically with proper weight coefficients, which indicates that the algorithm is optimal in the sense of asymptotic efficiency. Some numerical examples are simulated to demonstrate the effectiveness of the proposed algorithm in both first-order and high-order FIR systems.

System Identification With Binary-Valued Observations Under Data Tampering Attacks

With the popularization and application of cyber–physical systems in many industry and infrastructure fields, the security issue has been quite an important concern. This article addresses the defense problem against the data tampering attack under the framework of system identification with binary-valued observations. From the perspective of the attacker, it is shown that how to achieve the maximum hit effect with the least attack energy. From the perspective of the defender, a so-called compensation-oriented defense approach is proposed, and the corresponding identification algorithm is designed. The strong consistency of the algorithm is proved, and the asymptotic normality is obtained, based on which the optimal defense scheme is established. A simulation example is provided to illustrate the effectiveness of the defense algorithm and the main theoretical results.

A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations

A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations

Adaptive Tracking Control of FIR Systems Under Binary-Valued Observations and Recursive Projection Identification

In this article, adaptive tracking control of finite impulse response (FIR) systems is studied with binary-valued measurements. An adaptive control strategy is proposed based on an online identification algorithm. First, the designed control inputs are proved to be bounded and satisfy a persistent excitation (PE) condition under the assumption of periodic and PE target signals, which ensures the convergence of the identification algorithm. Second, the convergence rate of the identification algorithm is proved to be O(1/t) and it depends on the true parameter instead of a priori information of the parameter, which is more intuitive. Due to the convergence and the convergence rate of the identification algorithm, we finally prove that the adaptive tracking control is asymptotically optimal and the tracking speed is faster than the previous control algorithm. The simulations are given to validate the developed results in this article.