48 results about "Maximum entropy method" patented technology

A time series data analyzer includes a segment condition input section, an analysis condition input section, and an optimum condition deriving section for analyzing all segments based on the segment conditions and analysis conditions inputted in the respective input sections, under all analysis conditions by a maximum entropy method and a nonlinear least squares method. The time series data analyzer derives the optimum segment length and the optimum lag value in correspondence to selected results, and an analysis execution section executes analysis by the maximum entropy method by setting the optimum analysis conditions derived as described above. The trending of the spectrum of electroencephalogram data is used as an indicator of the state of the subject based on the findings that the spectrum of electroencephalogram data is an exponential spectrum and the gradient changes depending on the state of the subject.

The present invention relates to the field of traffic, especially to a maximum entropy method used for traffic subnetwork trip matrix estimation. The method comprises the following steps of: S1: selecting and establishing an abstracted sub traffic network, wherein the network is formed by a node set N and a road section set A, and the N comprises a starting point set R and a terminal point set S;S2: establishing and solving the maximum entropy model of a traffic subnetwork trip matrix; S3, in the abstracted sub traffic network, employing the maximum entropy model, performing initialization toobtain a feasible solution of the maximum entropy model, designing an algorithm to find and solve a current solution decreasing direction of decreasing of a target function value of the maximum entropy model; S4: performing linear search, performing solution, determining an optimal [Alpha], and determining the optimal step of the decreasing; S5: updating the feasible solution; and S6, allowing the algorithm to end the examination. The maximum entropy method used for the traffic subnetwork trip matrix estimation takes easily obtained flow of each road section of the whole network as unique input of the model to establish the maximum entropy problem so as to improve the algorithm efficiency, allow the method to be utilized in a large network and improve the prediction precision. The maximumentropy method used for the traffic subnetwork trip matrix estimation can be used for assessment of influences of different network changes on the subnetwork flow.

The present invention provides a method of selectivity estimation in which preprocessing steps improve the feasibility and efficiency of the estimation. The preprocessing steps are partitioning (to make iterative scaling estimation terminate in a reasonable time for even large sets of predicates), forced partitioning (to enable partitioning in case there are no “natural” partitions, by finding the subsets of predicates to create partitions that least impact the overall solution); inconsistency resolution (in order to ensure that there always is a correct and feasible solution), and implied zero elimination (to ensure convergence of the iterative scaling computation under all circumstances). All of these preprocessing steps make a maximum entropy method of selectivity estimation produce a correct cardinality model, for any kind of query with conjuncts of predicates. In addition, the preprocessing steps can also be used in conjunction with prior art methods for building a cardinality model.

The invention relates to a wind electricity maximum installed capacity prediction method which uses a maximum entropy principle. The wind electricity maximum installed capacity prediction method which uses the maximum entropy principle mainly achieves the purposes of achieving rapid and exact prediction for the wind electricity maximum installed capacity, and laying a foundation for reasonable planning of a power grid. The technical scheme is that the wind electricity maximum installed capacity prediction method which uses the maximum entropy principle includes steps: 1) building a chance constraint planning model of the wind electricity maximum installed capacity; 2) using a pattern search method combined with a maximum entropy method to solve the chance constraint planning model, and then obtaining prediction value of the wind electricity maximum installed capacity of a system. The electricity maximum installed capacity prediction method which uses the maximum entropy principle is mainly used in the wind electricity technology field.