Fuzzy logic inference processor: A custom VLSI design for system integration
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Watanabe, Hiroyuki↗
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Results for “logic”
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An application of fuzzy logic to robotic vision and control
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Hirota, Kaoru↗
Fuzzy logic operators and neuron activation fields
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Taber, Willard R.↗
A parallel algorithm for multi-level logic synthesis using the transduction method
The Transduction Method has been shown to be a powerful tool in the optimization of multilevel networks. Many tools such as the SYLON synthesis system (X90), (CM89), (LM90) have been developed based on this method. A parallel implementation is presented of SYLON-XTRANS (XM89) on an eight processor Encore Multimax shared memory multiprocessor. It minimizes multilevel networks consisting of simple gates through parallel pruning, gate substitution, gate merging, generalized gate substitution, and gate input reduction. This implementation, called Parallel TRANSduction (PTRANS), also uses partitioning to break large circuits up and performs inter- and intra-partition dynamic load balancing. With this, good speedups and high processor efficiencies are achievable without sacrificing the resulting circuit quality.
Lim, Chieng-Fai↗
Fuzzy logic controller for manipulator systems: Preliminary results
The following paper is presented in viewgraph format and covers topics that include: fuzzy control; center of gravity; and robotics.
Gerber, D.↗
Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations
As part of the Research Institute for Computing and Information Systems (RICIS) activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This interim report provides the status of the project and outlines the future plans.
Jani, Yashvant↗
Auto and hetero-associative memory using a 2-D optical logic gate
An optical system for auto-associative and hetero-associative recall utilizing Hamming distance as the similarity measure between a binary input image vector V(sup k) and a binary image vector V(sup m) in a first memory array using an optical Exclusive-OR gate for multiplication of each of a plurality of different binary image vectors in memory by the input image vector. After integrating the light of each product V(sup k) x V(sup m), a shortest Hamming distance detection electronics module determines which product has the lowest light intensity and emits a signal that activates a light emitting diode to illuminate a corresponding image vector in a second memory array for display. That corresponding image vector is identical to the memory image vector V(sup m) in the first memory array for auto-associative recall or related to it, such as by name, for hetero-associative recall.
Chao, Tien-Hsin↗
Application of programmable logic controllers to space simulation
Incorporating a state-of-the-art process control and instrumentation system into a complex system for thermal vacuum testing is discussed. The challenge was to connect several independent control systems provided by various vendors to a supervisory computer. This combination will sequentially control and monitor the process, collect the data, and transmit it to color a graphic system for subsequent manipulation. The vacuum system upgrade included: replacement of seventeen diffusion pumps with eight cryogenic pumps and one turbomolecular pump, replacing a relay based control system, replacing vacuum instrumentation, and upgrading the data acquisition system.
Sushon, Janet↗
Synthesis of nonlinear control strategies from fuzzy logic control algorithms
Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for nonlinearities in plant dynamics.
Langari, Reza↗
Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Translational controller results
The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.
Jani, Yashvant↗
Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Special approach/docking testcase results
As part of the RICIS project, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use these two terms interchangeably to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS) and programming/testing support from other contractor personnel. This report is the final deliverable D4 in our milestones and project activity. It provides the test results for the special testcase of approach/docking scenario for the shuttle and SMM satellite. Based on our experience and analysis with the attitude and translational controllers, we have modified the basic configuration of the reinforcement learning algorithm in ARIC. The shuttle translational controller and its implementation in ARIC is described in our deliverable D3. In order to simulate the final approach and docking operations, we have set-up this special testcase as described in section 2. The ARIC performance results for these operations are discussed in section 3 and conclusions are provided in section 4 along with the summary for the project.
Jani, Yashvant↗
Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Attitude control results
As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.
Jani, Yashvant↗
Min and max are the only continuous ampersand-, V-operations for finite logics
Experts usually express their degrees of belief in their statements by the words of a natural language (like 'maybe', 'perhaps', etc.). If an expert system contains the degrees of beliefs t(A) and t(B) that correspond to the statements A and B, and a user asks this expert system whether 'A&B' is true, then it is necessary to come up with a reasonable estimate for the degree of belief of A&B. The operation that processes t(A) and t(B) into such an estimate t(A&B) is called an &-operation. Many different &-operations have been proposed. Which of them to choose? This can be (in principle) done by interviewing experts and eliciting a &-operation from them, but such a process is very time-consuming and therefore, not always possible. So, usually, to choose a &-operation, the finite set of actually possible degrees of belief is extended to an infinite set (e.g., to an interval (0,1)), define an operation there, and then restrict this operation to the finite set. Only this original finite set is considered. It is shown that a reasonable assumption that an &-operation is continuous (i.e., that gradual change in t(A) and t(B) must lead to a gradual change in t(A&B)), uniquely determines min as an &-operation. Likewise, max is the only continuous V-operation. These results are in good accordance with the experimental analysis of 'and' and 'or' in human beliefs.
Kreinovich, Vladik↗
North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 1
This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such as neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.
Villarreal, James A.↗
Distributed traffic signal control using fuzzy logic
We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.
Chiu, Stephen↗
North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2
This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.
Villarreal, James A.↗
A fail-safe CMOS logic gate
This paper reports a design technique to make Complex CMOS Gates fail-safe for a class of faults. Two classes of faults are defined. The fail-safe design presented has limited fault-tolerance capability. Multiple faults are also covered.
Bobin, V.↗
On a logical basis for division of responsibilities in statistical practice
The purpose of this paper is to explain principles for division of responsibilities between the statistician and the people that he works with, and reasons why this division of responsibilities is important -- that is, how it improves the performance of both statistician and expert in subject-matter. The aim is to find and illustrate principles of practice by which statisticians may make effective use of their knowledge of theory. The specialist in statistical methods may find himself applying the same basic theory in a dozen different fields in a week, rotating through the same projects the next week. Or, he may work day after day primarily in a single substantive field. Either way, he requires rules of practice. A statement of statistical reliability should present any information that might help the reader to form his own opinion concerning the validity of conclusions likely to be drawn from the results. The aim of a statistical report is to protect the client from seeing merely what he would like to see; to protect him from losses that could come from misuse of results. A further aim is to forestall unwarranted claims of accuracy that the client's public might otherwise accept.
Deming, W. Edwards↗