Abstract: Described are method and apparatus for discriminating whether a time series data is a deterministic chaos or a stochastic chaos. In the method and apparatus, a trajectory parallel measuring method is used to discriminate the observed time series data.

Abstract: A system for predicting a blood glucose level is comprised of a time series measurement data storing section for storing blood glucose level measured data in a blood glucose time series file to treat the data as time series data. A dynamics estimating section estimates a dynamics which most preferably represents a phase characteristic of the time series data stored in the time series measurement data storing section. A parameter storing section stores an embedding dimension n and a time delay .tau. of the dynamics estimated in the dynamics estimating section as parameters for embedding the estimated dynamics in multidimensional state space. A blood glucose predicting section predicts a near future value of blood glucose level by means of the Local Fuzzy Reconstruction Method on the basis of the stored data of the blood glucose level data stored in the blood glucose time series file and the parameters corresponding to the data and for storing the predicted future value in a predicted blood glucose level file.

Abstract: A monitoring apparatus for monitoring an operating condition of a system includes a predicting section which generates a data vector whose parameter is determined by a timeseries data of the system and which obtains a prediction value of the timeseries data of a predetermined time future by means of the chaotic inference based on a behavior of attractor which is generated in a reconstruction space by an embedding operation of the data vector. A monitoring section compares the detected value and the prediction value of the timeseries data and decides the condition of the observed system according to the compared result. Therefore, it becomes possible to appropriately and quickly judge as to whether the observed system is in an abnormal condition or not.

Abstract: An apparatus for executing a short-term prediction of chaotic timeseries data includes a parameter determining section that changes each of parameter values according to the dynamics of observed chaotic timeseries data, and a predicting section that predicts a near future value of objective by reconstructing an attractor of timeseries data of the objective data in a multi-dimensional state space and by using vector neighboring to the data vector including the objective data. Therefore, it becomes possible to quickly and accurately execute a short-term prediction of chaotic timeseries data and to be responsive to the changes of the dynamics of the timeseries data.

Abstract: An apparatus and method for automatically generating fuzzy rules and/or membership functions for a fuzzy inference system are disclosed in which an input system, constituted by a computer system, is provided having a learning memory for storing learning data for all of input spaces, the input spaces being clustered for each fuzzy inference input variable and an output system, constituted by the computer system, is provided for determining a fuzzy label for each input variable, for dividing support sets of the respective input variables according to the determined fuzzy labels, for determining and storing a weight table of the computer system according to the number of divided support sets, for learning the computer system using the learning data on a basis of the fuzzy labels of the respective input variables, the weight table, and the number of divided support sets so that each representative point of the fuzzy labels of the input variables is input to the learned computer system to automatically derive at l

Abstract: An apparatus for carrying out fuzzy inference and a method therefor are disclosed in which an oblivescence coefficient is introduced and multiplied by a membership function of a conclusion portion in each operating variable linguistic control rule, the oblivescence coefficient being determined through a fuzzy inference according to an oblivescence element corresponding to a difference of times at which the linguistic control rule is established and at which it is used, and/or situations in which the linguistic control rule is established and in which it is used, and the determined oblivescence coefficient is used to vary a weight, or importance, of the linguistic control rule for the operation of the controlled object according thereto so that each linguistic control rule can flexibly be used to execute the fuzzy inference according to its used date, place, and object.