IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
US-0637406
(2000-08-11)
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발명자
/ 주소 |
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출원인 / 주소 |
- Maryland Technology Corporation
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인용정보 |
피인용 횟수 :
12 인용 특허 :
10 |
초록
▼
Active vibration control (AVC) systems without online path modeling and controller adjustment are provided that are able to adapt to an uncertain operating environment. The controller (250, 280, 315, 252, 282, 317, 254, 319) of such an AVC system is an adaptive recursive neural network whose weights
Active vibration control (AVC) systems without online path modeling and controller adjustment are provided that are able to adapt to an uncertain operating environment. The controller (250, 280, 315, 252, 282, 317, 254, 319) of such an AVC system is an adaptive recursive neural network whose weights are determined in an offline training and are held fixed online during the operation of the system. AVC feedforward, feedback, and feedforward-feedback systems in accordance with the present invention are described. An AVC feedforward system has no error sensor and an AVC feedback system has no reference sensor. All sensor outputs of an AVC system are processed by the controller for generating control signals to drive at least one secondary source (240). While an error sensor (480, 481) must be a vibrational sensor, a reference sensor (230, 270, 295, 305, 330) may be either a vibrational or nonvibrational sensor. The provided AVC systems reduce or eliminate most of such shortcomings of the prior-art AVC systems as use of an error sensor, relatively slow convergence of a weight/waveform adjustment algorithm, frequent adjustment of a path model, use of a high-order adaptive linear transversal filter, instability of an adaptive linear recursive filter, failure to use a useful nonvibrational reference sensor, failure to deal with the nonlinear behavior of a primary or secondary path, weight adjustment using control predicted values, use of an identification neural network, and online adjustment of the weights of a neural network.
대표청구항
▼
Active vibration control (AVC) systems without online path modeling and controller adjustment are provided that are able to adapt to an uncertain operating environment. The controller (250, 280, 315, 252, 282, 317, 254, 319) of such an AVC system is an adaptive recursive neural network whose weights
Active vibration control (AVC) systems without online path modeling and controller adjustment are provided that are able to adapt to an uncertain operating environment. The controller (250, 280, 315, 252, 282, 317, 254, 319) of such an AVC system is an adaptive recursive neural network whose weights are determined in an offline training and are held fixed online during the operation of the system. AVC feedforward, feedback, and feedforward-feedback systems in accordance with the present invention are described. An AVC feedforward system has no error sensor and an AVC feedback system has no reference sensor. All sensor outputs of an AVC system are processed by the controller for generating control signals to drive at least one secondary source (240). While an error sensor (480, 481) must be a vibrational sensor, a reference sensor (230, 270, 295, 305, 330) may be either a vibrational or nonvibrational sensor. The provided AVC systems reduce or eliminate most of such shortcomings of the prior-art AVC systems as use of an error sensor, relatively slow convergence of a weight/waveform adjustment algorithm, frequent adjustment of a path model, use of a high-order adaptive linear transversal filter, instability of an adaptive linear recursive filter, failure to use a useful nonvibrational reference sensor, failure to deal with the nonlinear behavior of a primary or secondary path, weight adjustment using control predicted values, use of an identification neural network, and online adjustment of the weights of a neural network. ineers, accountants, farmers, writers, managers and union leaders. 18. The program according to claim 1, further comprising a natural language interface process receiving input from the individual or smaller investor. 19. The program according to claim 1, further comprising an affinity group creation process creating an affinity group comprising a plurality of individuals having similar characteristics, the characteristics being selectable by the investor, said affinity group creation process determining what assets or liabilities have been purchased by the affinity group, and executing a purchase of the assets or liabilities that have been purchased by the affinity group. 20. A method using aggregation for creating and managing a portfolio of market tradable assets or liabilities, comprising the steps of: obtaining a plurality of investor preferences for characteristics of a plurality of distinct assets or liabilities for the portfolio of an investor; employing the plurality of investor preferences to select a plurality of distinct market tradable assets or liabilities to be owned directly by the investor and to be transacted in a market for each of the assets or liabilities in a plurality of transactions for the investor; aggregating the plurality of transactions of the investor with a plurality of transactions of a plurality of other investors over an applicable characteristic of the plurality of assets or liabilities, wherein said aggregating includes aggregating single shares, odd lots and/or fractional shares using a computer; and placing one or more trades based on said aggregating, wherein the selected plurality of distinct market tradable assets or liabilities are owned directly by the investor in the investor's portfolio as a result of said one or more trades. 21. The method according to claim 20, wherein the step of aggregating the plurality of transactions comprises the step of aggregating the plurality of transactions over a time period. 22. The method according to claim 21, wherein the time period includes every three hours. 23. The method according to claim 20, wherein aggregating th
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