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1. 发明申请

US20050197994A1 Intelligent robust control system for motorcycle using soft computing optimizer 失效
标题翻译：使用软计算优化器的摩托车智能鲁棒控制系统
公开(公告)号：US20050197994A1
公开(公告)日：2005-09-08
申请号：US10792292
申请日：2004-03-03
申请人： Shigeru Fujii , Hitoshi Watanabe , Sergey Panfilov , Kazuki Takahashi , Sergey Ulyanov
发明人： Shigeru Fujii , Hitoshi Watanabe , Sergey Panfilov , Kazuki Takahashi , Sergey Ulyanov
IPC分类号： B62J99/00 , B62K21/00 , G06N3/00 , G06F17/50 , B62D6/00 , G05B13/02 , G05D1/00 , G06F7/00 , G06F15/18 , G06F17/00 , G06F19/00 , G06N3/12 , G06N5/02 , G06N7/00
CPC分类号： B62K21/00
摘要： A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a motorcycle is described. In one embodiment, a simulation model of the motorcycle and rider control is used. In one embodiment, the simulation model includes a feedforward rider model. 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; 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.
摘要翻译：描述了用于设计用于控制摩托车的控制系统中使用的知识库（KB）的软计算（SC）优化器。在一个实施例中，使用摩托车和骑手控制的仿真模型。在一个实施例中，模拟模型包括前馈骑手模型。 SC优化器包括基于模糊神经网络（FNN）的模糊推理机。 SC优化器提供模糊推理系统（FIS）结构选择，FIS结构优化方法选择和教学信号选择与生成。用户选择模糊模型，包括以下一个或多个：输入和/或输出变量的数量; 模糊推理的类型; 和初步类型的会员职能。遗传算法（GA）用于优化语言变量参数和输入 - 输出训练模式。 GA也用于优化规则库，使用模糊模型，最优语言变量参数和教学信号。 GA产生近乎最佳的FNN。可以使用经典的基于导数的优化程序来改进近似最优的FNN。 GA发现的FIS结构通过基于受控植物实际植物模型的响应的适应度函数进行优化。 SC优化器产生通常比现有技术方法产生的KB更小的鲁棒KB。

2. 发明申请

US20050119986A1 Soft computing optimizer of intelligent control system structures 失效
标题翻译：智能控制系统结构的软计算优化器
公开(公告)号：US20050119986A1
公开(公告)日：2005-06-02
申请号：US10897978
申请日：2004-07-23
申请人： Sergey Panfilov , Ludmila Litvintseva , Sergey Ulyanov , Viktor Ulyanov , Kazuki Takahashi
发明人： Sergey Panfilov , Ludmila Litvintseva , Sergey Ulyanov , Viktor Ulyanov , Kazuki Takahashi
IPC分类号： G06F15/18 , G06F17/00 , G06G7/00 , G06N5/02 , G06N5/04 , G06N7/00 , G06N7/08 , H04N5/91
CPC分类号： G06N5/022 , G06N5/048
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.
摘要翻译：本发明涉及一种软计算（SC）优化器，用于设计用于控制诸如内燃机或汽车悬架系统的设备的控制系统中的知识库（KB）。 SC优化器包括基于模糊神经网络（FNN）的模糊推理机。 SC优化器提供模糊推理系统（FIS）结构选择，FIS结构优化方法选择和教学信号选择与生成。用户选择模糊模型，包括以下一个或多个：输入和/或输出变量的数量; 模糊推理模型的类型（例如，Mamdani，Sugeno，Tsukamoto等）; 和初步类型的会员职能。遗传算法（GA）用于优化语言变量参数和输入 - 输出训练模式。 GA也用于优化规则库，使用模糊模型，最优语言变量参数和教学信号。 GA产生近乎最佳的FNN。可以使用经典的基于导数的优化程序来改进近似最优的FNN。 GA发现的FIS结构通过基于受控植物实际植物模型的响应的适应度函数进行优化。 SC优化器产生通常比现有技术方法产生的KB更小的鲁棒KB。

3. 发明申请

US20060224547A1 Efficient simulation system of quantum algorithm gates on classical computer based on fast algorithm 审中-公开
标题翻译：基于快速算法的经典计算机上量子算法门的有效仿真系统
公开(公告)号：US20060224547A1
公开(公告)日：2006-10-05
申请号：US11089421
申请日：2005-03-24
申请人： Sergey Ulyanov , Sergey Panfilov
发明人： Sergey Ulyanov , Sergey Panfilov
IPC分类号： G06F15/18
CPC分类号： B82Y10/00 , G06N10/00
摘要： An efficient simulation system of quantum algorithm gates for classical computers with a Von Neumann architecture is described. In one embodiment, a Quantum Algorithm is solved using an algorithmic-based approach, wherein matrix elements of the quantum gate are calculated on demand. In one embodiment, a problem-oriented approach to implementing Grover's algorithm is provided with a termination condition determined by observation of Shannon minimum entropy. In one embodiment, a Quantum Control Algorithm is solved by using a reduced number of quantum operations.
摘要翻译：描述了具有Von Neumann架构的经典计算机的量子算法门的高效仿真系统。在一个实施例中，使用基于算法的方法来解决量子算法，其中根据需要计算量子门的矩阵元素。在一个实施例中，提供了实现Grover算法的面向问题的方法，其具有通过观察香农最小熵所确定的终止条件。在一个实施例中，通过使用减少量的量子操作来解决量子控制算法。

4. 发明申请

US20060218108A1 System for soft computing simulation 审中-公开
标题翻译：软件计算仿真系统
公开(公告)号：US20060218108A1
公开(公告)日：2006-09-28
申请号：US11243511
申请日：2005-10-04
申请人： Sergey Panfilov , Sergei Ulyanov
发明人： Sergey Panfilov , Sergei Ulyanov
IPC分类号： G06N3/12
CPC分类号： G06N5/025 , G05B13/0285 , G06N5/04
摘要： The present invention involves a Soft Computing Optimizer (SCOptimizer) for designing a Knowledge Base (KB) to be used in a control system for controlling a plant. The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and training 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, 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.
摘要翻译：本发明涉及一种用于设计用于控制工厂的控制系统中的知识库（KB）的软计算优化器（SCOptimizer）。 SC优化器提供模糊推理系统（FIS）结构选择，FIS结构优化方法选择和训练信号选择与生成。用户选择模糊模型，包括以下一个或多个：输入和/或输出变量的数量; 模糊推理模型的类型（例如，Mamdani，Sugeno等）; 和初步类型的会员职能。遗传算法（GA）用于优化语言变量参数和输入 - 输出训练模式。 GA也用于优化规则库，使用模糊模型，最优语言变量参数和教学信号。

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