The realization of transfer functions with logical networks
Techniques for logical network synthesis with given transfer function based on approximation methods using sampled inputs
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A computer program for the generation of logic networks from task chart data
The Network Generation Program (NETGEN), which creates logic networks from task chart data is presented. NETGEN is written in CDC FORTRAN IV (Extended) and runs in a batch mode on the CDC 6000 and CYBER 170 series computers. Data is input via a two-card format and contains information regarding the specific tasks in a project. From this data, NETGEN constructs a logic network of related activities with each activity having unique predecessor and successor nodes, activity duration, descriptions, etc. NETGEN then prepares this data on two files that can be used in the Project Planning Analysis and Reporting System Batch Network Scheduling program and the EZPERT graphics program.
Herbert, H. E.↗
Mental Models of Invisible Logical Networks
Subjects were required to discover the structure of a logical network whose links were invisible. Network structure had to be inferred from the behavior of the components after a failure. It was hypothesized that since such failure diagnosis tasks often draw on spatial processes, a good deal of spatial complexity in the network should affect network discovery. Results show that the ability to discover the linkages in the network is directly related to the spatial complexity of the pathway described by the linkages. This effect was generally independent of the amount of evidence available to subjects about the existence of the link. These results raise the question of whether inferences about spatially complex pathways were simply not made, or whether they were made but not retained because of a high load on memory resources.
Sanderson, P.↗
Adaptive parallel logic networks
Adaptive, self-organizing concurrent systems (ASOCS) that combine self-organization with massive parallelism for such applications as adaptive logic devices, robotics, process control, and system malfunction management, are presently discussed. In ASOCS, an adaptive network composed of many simple computing elements operating in combinational and asynchronous fashion is used and problems are specified by presenting if-then rules to the system in the form of Boolean conjunctions. During data processing, which is a different operational phase from adaptation, the network acts as a parallel hardware circuit.
Martinez, Tony R.↗
Toward an algebraic theory of the analysis and testing of digital networks.
Logic networks of Boolean analogs analyzed by algebraic signal flow theory, introducing variational derivative for test detection in combinational and sequential networks
Brown, A.↗
Logic hazards in threshold networks.
Logic hazards detection in threshold gate networks, discussing hazard free network synthesis based on Boolean functions and tree method
Coates, C. L.↗
Implementing neural nets with programmable logic
Networks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.
Vidal, Jacques J.↗
Fuzzy logic, neural networks, and soft computing
The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.
Zadeh, Lofti A.↗
Four phase logic systems
A four-phase logic system is provided which includes at least four logic networks connected in parallel between a single power line and a reference potential. A four-phase clock generator generates four distinct clock signals from a single-phase clock input at data rate. Each logic network comprises a pair of complementary metal-oxide-semiconductor integrated transistors (CMOST). Each metal-oxide-Semiconductor transistor (MOST) in the pair is responsive to a clock signal which turns the transistor on or off. In each network, there is also at least one MOST which is responsive to a logic signal. The logic transistor is connected in cascade with the pair of CMOSTs.
Petersen, H. L.↗
Deep Space Network Antenna Logic Controller
The Antenna Logic Controller (ALC) software controls and monitors the motion control equipment of the 4,000-metric-ton structure of the Deep Space Network 70-meter antenna. This program coordinates the control of 42 hydraulic pumps, while monitoring several interlocks for personnel and equipment safety. Remote operation of the ALC runs via the Antenna Monitor & Control (AMC) computer, which orchestrates the tracking functions of the entire antenna. This software provides a graphical user interface for local control, monitoring, and identification of faults as well as, at a high level, providing for the digital control of the axis brakes so that the servo of the AMC may control the motion of the antenna. Specific functions of the ALC also include routines for startup in cold weather, controlled shutdown for both normal and fault situations, and pump switching on failure. The increased monitoring, the ability to trend key performance characteristics, the improved fault detection and recovery, the centralization of all control at a single panel, and the simplification of the user interface have all reduced the required workforce to run 70-meter antennas. The ALC also increases the antenna availability by reducing the time required to start up the antenna, to diagnose faults, and by providing additional insight into the performance of key parameters that aid in preventive maintenance to avoid key element failure. The ALC User Display (AUD) is a graphical user interface with hierarchical display structure, which provides high-level status information to the operation of the ALC, as well as detailed information for virtually all aspects of the ALC via drill-down displays. The operational status of an item, be it a function or assembly, is shown in the higher-level display. By pressing the item on the display screen, a new screen opens to show more detail of the function/assembly. Navigation tools and the map button allow immediate access to all screens.
Ahlstrom, Harlow↗
Logic-controlled occlusive cuff system
An occlusive cuff system comprises a pressure cuff and a source of regulated compressed gas feeding the cuff through an electrically operated fill valve. An electrically operated vent valve vents the cuff to the ambient pressure. The fill valve is normally closed and the vent valve is normally open. In response to an external start signal, a logic network opens the fill valve and closes the vent valve, thereby starting the pressurization cycle and a timer. A pressure transducer continuously monitors the pressure in the cuff. When the transducer's output equals a selected reference voltage, a comparator causes the logic network to close the fill valve. The timer, after a selected time delay, opens the vent valve to the ambient pressure, thereby ending the pressurization cycle.
Baker, J. T.↗
NASA Planning for Orion Multi-Purpose Crew Vehicle Ground Operations
The NASA Orion Ground Processing Team was originally formed by the Kennedy Space Center (KSC) Constellation (Cx) Project Office's Orion Division to define, refine and mature pre-launch and post-landing ground operations for the Orion human spacecraft. The multidisciplined KSC Orion team consisted of KSC civil servant, SAIC, Productivity Apex, Inc. and Boeing-CAPPS engineers, project managers and safety engineers, as well as engineers from Constellation's Orion Project and Lockheed Martin Orion Prime contractor. The team evaluated the Orion design configurations as the spacecraft concept matured between Systems Design Review (SDR), Systems Requirement Review (SRR) and Preliminary Design Review (PDR). The team functionally decomposed prelaunch and post-landing steps at three levels' of detail, or tiers, beginning with functional flow block diagrams (FFBDs). The third tier FFBDs were used to build logic networks and nominal timelines. Orion ground support equipment (GSE) was identified and mapped to each step. This information was subsequently used in developing lower level operations steps in a Ground Operations Planning Document PDR product. Subject matter experts for each spacecraft and GSE subsystem were used to define 5th - 95th percentile processing times for each FFBD step, using the Delphi Method. Discrete event simulations used this information and the logic network to provide processing timeline confidence intervals for launch rate assessments. The team also used the capabilities of the KSC Visualization Lab, the FFBDs and knowledge of the spacecraft, GSE and facilities to build visualizations of Orion pre-launch and postlanding processing at KSC. Visualizations were a powerful tool for communicating planned operations within the KSC community (i.e., Ground Systems design team), and externally to the Orion Project, Lockheed Martin spacecraft designers and other Constellation Program stakeholders during the SRR to PDR timeframe. Other operations planning tools included Kaizen/Lean events, mockups and human factors analysis. The majority of products developed by this team are applicable as KSC prepares 21st Century Ground Systems for the Orion Multi-Purpose Crew Vehicle and Space Launch System.
Letchworth, Gary↗
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↗
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↗
Nuclear flight system definition study, phase 3. Volume 5 - Schedules, milestones and networks Final report
Reusable nuclear shuttle program schedules and logic networks of tasks, sequencing, and interfaces
Antypas, W. G.↗
Fuzzy logic and neural network technologies
Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.
Villarreal, James A.↗