IPC분류정보
국가/구분 |
United States(US) Patent
등록
|
국제특허분류(IPC7판) |
|
출원번호 |
US-0403318
(2000-01-18)
|
우선권정보 |
JP-0056101 (1998-02-20) |
국제출원번호 |
PCT/JP99/00724
(1999-02-18)
|
국제공개번호 |
WO99/42928
(1999-08-26)
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발명자
/ 주소 |
|
출원인 / 주소 |
- Sowa Institute of Technology Co., Ltd.
|
대리인 / 주소 |
|
인용정보 |
피인용 횟수 :
0 인용 특허 :
5 |
초록
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A binary learning system characterized by consisting of an input layer having binary input terminals, a coupling layer, a first binary gate layer with first similar logical elements, a second binary gate layer with second similar logical elements, and an output layer, so as to form a learning networ
A binary learning system characterized by consisting of an input layer having binary input terminals, a coupling layer, a first binary gate layer with first similar logical elements, a second binary gate layer with second similar logical elements, and an output layer, so as to form a learning network, in that each coupling condition between the adjacent layers limited to one way directing from their inlet side to the outlet side, and each layer has independent routes without mutual coupling conditions, the coupling layer having means for selecting either one of a direct coupling condition and a coupling condition routed through an inverter, relative to routes from the respective signal units in the input layer to the respective signal units in the first binary gate layer, in such manner that the selected coupling condition is adapted to eliminate or decrease the respective errors between original output signals at the output layer and monitor signals in the learning network.
대표청구항
▼
A binary learning system characterized by consisting of an input layer having binary input terminals, a coupling layer, a first binary gate layer with first similar logical elements, a second binary gate layer with second similar logical elements, and an output layer, so as to form a learning networ
A binary learning system characterized by consisting of an input layer having binary input terminals, a coupling layer, a first binary gate layer with first similar logical elements, a second binary gate layer with second similar logical elements, and an output layer, so as to form a learning network, in that each coupling condition between the adjacent layers limited to one way directing from their inlet side to the outlet side, and each layer has independent routes without mutual coupling conditions, the coupling layer having means for selecting either one of a direct coupling condition and a coupling condition routed through an inverter, relative to routes from the respective signal units in the input layer to the respective signal units in the first binary gate layer, in such manner that the selected coupling condition is adapted to eliminate or decrease the respective errors between original output signals at the output layer and monitor signals in the learning network. ed tracking error signal of the compensator is uniformly ultimately bounded. 9. The compensator of claim 8, wherein the means for tuning comprises a Hebbian tuning algorithm. 10. The compensator of claim 8, wherein the mechanical system comprises an actuator or robot. 11. An adaptive neural network compensator for compensating backlash of a mechanical system, comprising: a feedforward path; a proportional derivation tracking loop comprising a proportional derivative path in the feedforward path; a neural network in the feedforward path; and a nonlinear estimate feedback loop coupled to the feedforward path. 12. The compensator of claim 11, wherein the neural network the neural network is tuned according a Hebbian tuning algorithm. 13. The compensator of claim 11, wherein the mechanical system comprises an actuator or robot. 14. A method of adaptively compensating backlash in a mechanical system, comprising: estimating an inverse of the backlash using a neural network in a feedforward path; taking a filtered derivative of a tracking error signal of the compensator using a filter in the feedforward path to form a filtered tracking error signal; adjusting weights of the neural network as a function of the filtered tracking error signal using a Hebbian tuning algorithm to achieve closed loop stability; and applying the inverse to an input of the mechanical system to compensate the backlash. 15. The method of claim 14, wherein the filtered tracking error is uniformly ultimately bounded. 16. The method of claim 14, wherein weight estimates of the neural network are uniformly ultimately bounded. 17. The method of claim 14, wherein weight estimates of the neural network and the filtered tracking error are uniformly ultimately bounded. 18. The method of claim 14, wherein initial weights V of the neural network are selected randomly and initial weights W of the neural network are set to zero. 19. The method of claim 18, further comprising providing stable feedback control of the mechanical system while weights of the neural network are adjusted from initialization values with a proportional derivative tracking loop comprising a proportional derivative path in the feedforward path. 20. The method of claim 14, wherein the mechanical system comprises an actuator or robot.
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