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 Wilamowskidescription：该项目支持一个合作研究项目，位于奥本大学的奥本大学电气和计算机工程系Bogdan Wilamowski博士之间，阿拉巴马州奥本大学和Turkey伊斯坦布尔的Bogazici大学的Okyay Kaynak博士。 他们计划专注于计算智能系统的硬件实施，目的是从识别和控制机场系统的识别和控制的角度研究计算智能体系结构的实时和芯片实施。第二个目标是使用专用的VLSI芯片实施要制定的控制策略，该芯片可以是模拟或数字化的。 由于计算机技术的持续开发可能是由于大量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 - 智能行为取决于建筑的灵活性，实现人类专业知识的机器纳入能力，推理程序定律和学习速度。 所有这些都是名为计算智能的研究领域的主要组成部分，而该区域的软分细分是人工神经网络和模糊推理系统。 机器智能的数学力量通常归因于所使用的神经样系统体系结构以及由大量互连结构产生的容错。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“一个”数字级别，而这样的方式将通过系统传递更多信息。传统的数字计算机不太适合此类信号处理。 计算上智能的方法通常用于识别和/或对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可变形式的非线性微分方程来描述。可以在硅中轻松实现集成商。硬件实现的困难任务是实施任意的非线性术语。模糊或神经系统是实现此目的的主要候选人。 范围：PIS建议在Bogazici大学在控制和软计算领域的实用，工业和理论经验与Auburn University的神经和模糊系统中的理论经验相结合。 土耳其群体在智能标识符和控制器的设计方面具有大量的知识和经验。另一方面，美国方面具有神经网络架构，其比较特征和VLSI设计方面的专业知识。来自美国的小组在设计VLSI芯片方面具有理论和实际实施经验。预计这两组的合作将导致物理上可实现和有用的产品，并通过ONCHIP实现促进实时识别和控制非线性系统的程序。 该项目以拟议的访问和预期的结果，将为双方带来互惠互利。土耳其一方将受益于美国方面的经验，发展了对计算上智能建筑的芯片实现，而美国方面将受益于已在Bogazici大学获得的知识和经验中受益。