Using Kalman filtering method, based on Riccati equation, under the linear minimum variance optimal information fusion criterions weighted by scalars, for the single channel ARMA signals with white and colored measurement noises, the multisensor distributed information fusion Wiener filters is presented.
Using the Kalman filtering method, based on the Riccati equation, under the linear minimum variance optimal information fusion criterion, the information fusion Kalman filter and smoother weighted by matrices are presented for the two-sensor system with the correlated input and observation noises, and with the correlated observation noises.
So, This text take it as example that distributor offers the information sharing to manufacturers, through building the model, studied their own optimum information sharing degree in 3 kinds of possible systems, compared arrangement impact on the variables,such as optimum information sharing degree,profit,etc. and has pointed out the optimum arrangement separately to the manufacturer , distributor and the whole supply chain.
This paper adopts an adaptive learning algorithm based on hierarchy clustering to update user profile,which continuously abstract the cancroids of one class of optimum information from the feedback flow of system,which effectively shield the learning process from plenty of feedback noises produced by distorted threshold and sparseness of initial information,which also can imitate artificial feedback approximately to perfect the intelligence of adaptive learning mechanism.
By using the Kalman filtering method and the linear minimum variance optimal fusion rule weighted by matrices,a multisensor information fusion Wiener filter is presented for the multichannel autoregressive moving average(ARMA) signals with white observation noise.
Based on the Kalman filtering method and white noise estimation theory,under linear minimum variance information fusion criterion weighted by matrices,a multisensor information fusion white noise deconvolution filter is presented for systems with correlated noises.
Using the Kalman filtering method, based on white noise estimation theory, under the linear minimum variance information fusion criterion, two-sensor information fusion steady-state optimal Wiener filter, smoother and predictor are presented for the multichannel Auto-Regressive Moving Average(ARMA) signals, where the optimal weighting matrices and minimum fused error variance matrix are given.
An architecture is then proposed consisting of two agents, a requesting agent and a responding agent, and a communication language and protocol with which these agents can interact to promote optimal information exchange while respecting the law.
The optimal information-seeking strategy is evaluated for a neutral risk taker.
CT, including enhancement and intrathecal metrizamide, did not yield optimal information about exact localisation or infiltrative growth of lesions.
The transform coefficients are computed through a proper orthogonal decomposition, providing complete data decorrelation and optimal information compression.
We have analyzed the various connection possibilities from the standpoint of optimal information transmission by multiple channels.
The information exchanging technique which makes the completenss of the decentralized information structure based on the evaluation of its incompleteness is considered. The most complex case in which every channel can transmit only one-dimensional infor-mation is studied, and an recursive designing method is proposed.
Based on maximizing oriented energy of signals, this paper presents a novel algorithm, the generalized inverse projection (GIP), to locate k faults in analog circuits with tolerance. The GIP consists of formation of a matrix equivalent to tolerance disturbance, determination of an optimal information subspace, and inverse projection. Analysis and simulation have shown that this algorithm is superior to other published ones in the on - line computation requirement and robustness for k - faults location.
The concept of resources allocation,Pareto optimum and information resources allocation are explained.The prerequisites and methods to improve information resources allocation efficiency are explored.And then,the efficiency of information production and information market,the objective of information policy,and the adjustment of industrial structure are systematically analyzed.Based on these,the frame of information resources allocaion research is constructed.