This paper investigates the differentially private bipartite consensus problem over signed networks. To solve this problem, a new algorithm is proposed by adding noises with time-varying variances to cooperative-competitive interactive information. In order achieve privacy protection, of added are allowed increase, which substantially different from existing works. addition, can be either decaying or constant. By using step-size based on stochastic approximation method, we show that...

This paper investigates the online identification problem of binary-valued moving average systems. A stochastic approximation-based algorithm without projections or truncations is proposed. To analyze convergence property algorithm, distribution tail parameter estimate proved to be exponentially convergent through an auxiliary process. Under uniform persistent excitations, almost sure and mean square obtained. When step-size coefficient properly selected, rates are reach <inline-formula...

This letter investigates the differentiated output-based privacy-preserving average consensus problem over digraphs. A new stochastic obfuscation algorithm is proposed to achieve better effect. When output messages for at least one out-neighbour are not leaked, can be designed any pre-given accuracy and level simultaneously by properly selecting private weights. Even if all still ensure that each agent’s initial state protected a certain extent. The mean square convergence of proved....

This paper investigates the differentially private bipartite consensus algorithm over signed networks. The proposed protects each agent's sensitive information by adding noise with time-varying variances to cooperative-competitive interactive information. In order achieve privacy protection, variance of added is allowed be increased, and substantially different from existing works. addition, can either decaying or constant. By using step-sizes based on stochastic approximation method, we...

This paper investigates the joint identification problem of unknown system parameter and noise parameters in quantized systems when noises involved are Gaussian with variance mean value. Under such noises, previous investigations show that not jointly identifiable single-threshold quantizer case. The identifiability multi-threshold case still remains an open problem. proves parameter, value if only there at least two thresholds. Then, a decomposition-recombination algorithm is proposed to...

The paper investigates the distributed estimation problem under low bit rate communications. Based on signal-comparison (SC) consensus protocol binary-valued communications, a new consensus+innovations type algorithm is proposed. Firstly, high-dimensional estimates are compressed into messages by using periodic compressive strategy, dithered noises and sign function. Next, based expanding triggering thresholds, stochastic event-triggered mechanism proposed to reduce communication frequency....

This paper studies the control-oriented identification problem of set-valued moving average systems with uniform persistent excitations and observation noises. A stochastic approximation-based (SA-based) algorithm without projections or truncations is proposed. The overcomes limitations existing empirical measurement method recursive projection method, where former requires periodic inputs, latter to restrict search region in a compact set.To analyze convergence property algorithm,...

The average consensus problem under binary-valued communications is investigated in the paper. Measurement noises and fixed quantizers are considered. A signal comparison algorithm proposed for problem. Neither noise distribution information nor assumptions on states' approximate locations required design. proved to achieve both almost sure mean square sense. algorithm's convergence rate also calculated. efficiency of demonstrated by a numerical example.