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1. 发明申请

WO2005013019A3 SOFT COMPUTING OPTIMIZER OF INTELLIGENT CONTROL SYSTEM STRUCTURES 审中-公开
公开(公告)号：WO2005013019A3
公开(公告)日：2005-02-10
申请号：PCT/US2004/023970
申请日：2004-07-23
申请人： YAMAHA MOTOR CO., LTD , YAMAHA MOTOR CORP., USA , ULYANOV, Sergei, V. , PANFILOV, Sergei, A. , LITVINTSEVA, Ludmila , ULYANOV, Viktor, S. , TAKAHASHI, Kazuki
发明人： ULYANOV, Sergei, V. , PANFILOV, Sergei, A. , LITVINTSEVA, Ludmila , ULYANOV, Viktor, S. , TAKAHASHI, Kazuki
IPC分类号： G06N5/02 , G05N5/04
摘要： The present invention involves a Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a plant such as, for example, an internal combustion engine or an automobile suspension system. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and/or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual plant model of the controlled plant. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.

2. 发明申请

WO2004025137A3 FUZZY CONTROLLER WITH A REDUCED NUMBER OF SENSORS 审中-公开
公开(公告)号：WO2004025137A3
公开(公告)日：2004-03-25
申请号：PCT/US2003/028999
申请日：2003-09-15
申请人： YAMAHA MOTOR CO., LTD. , YAMAHA MOTOR CORP., USA
发明人： HAGIWARA, Takahide , ULYANOV, Sergei, V. , PANFILOV, Sergei, A. , TAKAHASHI, Kazuki , KANEKO, Chikako , DIAMANTE, Olga
IPC分类号： G06F7/00
摘要： A control system for optimizing the performance of a vehicle suspension system by controlling the damping factor of one or more shock absorbers is described. In one embodiment, the control system uses a fuzzy neural network (1301). A teaching signal (1006) for the fuzzy neural network is generated using road signal data and a mathematical model of the vehicle suspension system. The teaching signal is used to develop a knowledge base (1302) for the fuzzy neural network. In one embodiment, inputs to the fuzzy neural network include damper velocities, heave acceleration, pitch acceleration, and roll acceleration. In one embodiment, the heave acceleration signal from the teaching signal is filtered to develop inputs for the fuzzy neural network, thereby reducing the number of sensors. In one embodiment, a Fourier transform analysis of the heave acceleration signal is provided to the fuzzy neural network.

3. 发明申请

WO2004012139A2 INTELLIGENT MECHATRONIC CONTROL SUSPENSION SYSTEM BASED ON QUANTUM SOFT COMPUTING 审中-公开
标题翻译：基于量子软计算的智能机械控制悬架系统
公开(公告)号：WO2004012139A2
公开(公告)日：2004-02-05
申请号：PCT/US2003/023727
申请日：2003-07-29
申请人： YAMAHA MOTOR CO., LTD , YAMAHA MOTOR CORP., USA
发明人： ULYANOV, Sergei, V. , PANFILOV, Sergei , HAGIWARA, Takahide , TAKAHASHI, Kazuki , LITVINTSEVA, Ludmila
IPC分类号： G06N1/00
CPC分类号： G06N99/002 , B82Y10/00
摘要： A control system for optimizing a shock absorber having a non-linear kinetic characteristic is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy and biologically inspired constraints relating to mechanical constraints and/or rider comfort, driveability, etc. In one embodiment, a genetic analyzer is used in an off-line mode to develop a teaching signal. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from a knowledge base. A learning system is used to create the knowledge base for use by the online fuzzy controller. In one embodiment, the learning system uses a quantum search algorithm to search a number of solution spaces to obtain information for the knowledge base. The online fuzzy controller is used to program a linear controller.
摘要翻译：描述了一种用于优化具有非线性动力特性的减震器的控制系统。控制系统使用基于最小熵的物理定律和与机械约束和/或骑手舒适性，驾驶性能等有关的生物学启发约束的适应性（性能）功能。在一个实施例中，将遗传分析仪用于关闭线模式开发教学信号。教学信号可以通过使用来自知识库的知识进行操作的模糊控制器在线近似。学习系统用于创建在线模糊控制器使用的知识库。在一个实施例中，学习系统使用量子搜索算法来搜索多个解空间以获得知识库的信息。在线模糊控制器用于编程线性控制器。

4. 发明申请

WO2004012021A1 SYSTEM AND METHOD FOR NONLINEAR DYNAMIC CONTROL BASED ON SOFT COMPUTING WITH DISCRETE CONSTRAINTS 审中-公开
标题翻译：基于具有离散约束的软计算的非线性动态控制系统与方法
公开(公告)号：WO2004012021A1
公开(公告)日：2004-02-05
申请号：PCT/US2003/023671
申请日：2003-07-29
申请人： YAMAHA MOTOR CO., LTD
发明人： ULYANOV, Sergei, V. , PANFILOV, Sergei , TAKAHASHI, Kazuki
IPC分类号： G05B13/02
CPC分类号： G05B13/0285
摘要： A control system using a genetic analyzer based on discrete constraints is described. In one embodiment, a genetic algorithm with step-coded chromosomes is used to develop a teaching signal that provides good control qualities for a controller with discrete constraints, such as, for example, a step-constrained controller. In one embodiment, the control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. In one embodiment, the genetic analyzer is used in an off-line mode to develop a teaching signal for a fuzzy logic classifier system that develops a knowledge base. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from the knowledge base. The control system can be used to control complex plants described by nonlinear, unstable, dissipative models. In one embodiment, the step-constrained control system is configured to control stepping motors.
摘要翻译：描述了使用基于离散约束的遗传分析仪的控制系统。在一个实施例中，使用具有步进编码的染色体的遗传算法来开发教学信号，其为具有离散约束的控制器提供良好的控制质量，例如步进约束控制器。在一个实施例中，控制系统使用基于最小熵的物理定律的适应性（性能）功能。在一个实施方案中，遗传分析仪用于离线模式以开发用于开发知识库的模糊逻辑分类器系统的教学信号。教学信号可以通过使用来自知识库的知识进行操作的模糊控制器在线近似。控制系统可用于控制由非线性，不稳定，耗散模型描述的复杂工厂。在一个实施例中，步进约束控制系统被配置为控制步进电机。

5. 发明公开

EP1540430A2 SYSTEM AND METHOD FOR NONLINEAR DYNAMIC CONTROL BASED ON SOFT COMPUTING WITH DISCRETE CONSTRAINTS 审中-公开
标题翻译：基于带有离散不利条件SOFT计算系统和方法非线性动力学系统
公开(公告)号：EP1540430A2
公开(公告)日：2005-06-15
申请号：EP03772029.9
申请日：2003-07-29
申请人： Yamaha Motor Co., Ltd.
发明人： ULYANOV, Sergei V. , PANFILOV, Sergei , TAKAHASHI, Kazuki
IPC分类号： G05B13/02
CPC分类号： G05B13/0285
摘要： A control system using a genetic analyzer based on discrete constraints is described. In one embodiment, a genetic algorithm with step-coded chromosomes is used to develop a teaching signal that provides good control qualities for a controller with discrete constraints, such as, for example, a step-constrained controller. In one embodiment, the control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. In one embodiment, the genetic analyzer is used in an off-line mode to develop a teaching signal for a fuzzy logic classifier system that develops a knowledge base. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from the knowledge base. The control system can be used to control complex plants described by nonlinear, unstable, dissipative models. In one embodiment, the step-constrained control system is configured to control stepping motors.

6. 发明公开

EP1525555A2 INTELLIGENT MECHATRONIC CONTROL SUSPENSION SYSTEM BASED ON QUANTUM SOFT COMPUTING 审中-公开
标题翻译：智能机电一体化悬挂控制系统的使用量子软计算
公开(公告)号：EP1525555A2
公开(公告)日：2005-04-27
申请号：EP03772055.4
申请日：2003-07-29
申请人： Yamaha Motor Co., Ltd.
发明人： ULYANOV, S.V.Polo Didattico E Di Recerca Di Crema , PANFILOV, S , HAGIWARA, Takahide , TAKAHASHI, Kazuki , LITVINTSEVA, Ludmila , ULYANOV, Viktor, S.
IPC分类号： G06N1/00
CPC分类号： G06N99/002 , B82Y10/00
摘要： A control system for optimizing a shock absorber having a non-linear kinetic characteristic is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy and biologically inspired constraints relating to mechanical constraints and/or rider comfort, driveability, etc. In one embodiment, a genetic analyzer is used in an off-line mode to develop a teaching signal. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from a knowledge base. A learning system is used to create the knowledge base for use by the online fuzzy controller. In one embodiment, the learning system uses a quantum search algorithm to search a number of solution spaces to obtain information for the knowledge base. The online fuzzy controller is used to program a linear controller.

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