U.S.-Turkey Cooperative Research: Silicon Implementation of Computational Intelligence for Mechatronics

美国-土耳其合作研究：机电一体化计算智能的硅实现

• 批准号: 0352771
• 负责人: Bogdan Wilamowski
• 金额: --
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-15 至 2008-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0352771&HistoricalAwards=false
• 关键词: U.S.; Turkey; Cooperative; Research; Silicon

0352771 WilamowskiDescription: This project supports a cooperative research project between Dr. Bogdan Wilamowski, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama and Dr. Okyay Kaynak, Bogazici University in Istanbul, Turkey. They plan to focus on hardware implementation of computationally intelligent systems, with the aim to investigate real-time and on-chip implementation of computationally intelligent architectures from the point of view of identification and control of mechatronic systems. A second objective is to implement the control strategies to be developed, using dedicated VLSI chips, which can be analog or digital. As a likely result of the on-going development of computer technology it is expected that massive parallel processing and soft computing will significantly enhance traditional computation methods. A natural consequence of this rapid growth is the emergence of the field of intelligent systems. The machine-intelligent behavior is determined by the flexibility of the architecture, the ability to realize machine incorporations of human expertise, laws of inference procedure and the speed of learning. All these are the main constituents of the research area named Computational Intelligence, while the soft subdivisions of the area are artificial neural networks and fuzzy inference systems. The mathematical power of machine intelligence is commonly attributed to the neural-like system architecture used and the fault tolerance arising from the massively interconnected structure. Another aspect of soft computing systems is that instead of "zero" and "one" digital levels, they use fuzzy/continuous levels and in this way much more information is passed through the system. Conventional digital computers are not well suited for such signal processing. Computationally intelligent methodologies are commonly used for identification and/or control of nonlinear dynamic systems, the behavior of which can usually be described by a set of nonlinear differential equations in state variable form. Integrators can easily be implemented in silicon. The difficult task in hardware realization is to implement arbitrary nonlinear terms. Fuzzy or neural systems are the prime candidates for that purpose. Scope: The PIs propose to synergistically combine the practical, industrial and theoretical experience at Bogazici University in the area of control and soft computing with theoretical experience in neural and fuzzy systems at Auburn University. The Turkish group has considerable amount of knowledge and experience in the design of intelligent identifiers and controllers. On the other hand, the U.S. side has the expertise in neural network architectures, their comparative characteristics and VLSI design. The group from the USA, has theoretical and actual implementation experience in designing VLSI chips. The co-operation of these two groups is expected to result in physically realizable and useful products and facilitate the procedure of real-time identification and control of nonlinear systems by onchip realization. The project, with the proposed visits and expected outcomes, will bring mutual benefits to both sides. The Turkish side will benefit from the experience of the American side developing on-chip realization of computationally intelligent architectures, whereas the American side will benefit from the knowledge and experience that has already been acquired at Bogazici University.

0352771 Wilamowski 描述：该项目支持阿拉巴马州奥本大学电气与计算机工程系的 Bogdan Wilamowski 博士和土耳其伊斯坦布尔 Bogazici 大学的 Okyay Kaynak 博士之间的合作研究项目。 他们计划重点研究计算智能系统的硬件实现，旨在从机电系统识别和控制的角度研究计算智能架构的实时和片上实现。第二个目标是使用专用的 VLSI 芯片（可以是模拟的或数字的）来实施要开发的控制策略。 作为计算机技术持续发展的可能结果，预计大规模并行处理和软计算将显着增强传统计算方法。 这种快速增长的自然结果是智能系统领域的出现。机器智能行为取决于架构的灵活性、实现机器融合人类专业知识的能力、推理过程的规律和学习速度。 这些都是计算智能研究领域的主要组成部分，而该领域的软细分是人工神经网络和模糊推理系统。 机器智能的数学能力通常归因于所使用的类神经系统架构以及大规模互连结构所产生的容错能力。软计算系统的另一个方面是，它们使用模糊/连续级别，而不是“零”和“一”数字级别，通过这种方式，更多的信息通过系统传递。传统的数字计算机不太适合这种信号处理。 计算智能方法通常用于非线性动态系统的识别和/或控制，其行为通常可以通过一组状态变量形式的非线性微分方程来描述。积分器可以轻松地在硅中实现。硬件实现中的困难任务是实现任意非线性项。模糊或神经系统是实现这一目的的主要候选者。 范围：PI建议将海峡大学在控制和软计算领域的实践、工业和理论经验与奥本大学在神经和模糊系统方面的理论经验相结合。 土耳其集团在智能识别器和控制器的设计方面拥有丰富的知识和经验。另一方面，美国方面在神经网络架构、其比较特性和VLSI设计方面拥有专业知识。该团队来自美国，具有VLSI芯片设计的理论和实际实现经验。这两个小组的合作预计将产生物理上可实现且有用的产品，并促进通过片上实现对非线性系统进行实时识别和控制的过程。 该项目的拟定访问和预期成果将为双方带来互惠互利。土耳其方面将受益于美国方面开发计算智能架构片上实现的经验，而美国方面将受益于海峡大学已经获得的知识和经验。