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NASA NTRS2008

Method and associated apparatus for capturing, servicing and de-orbiting earth satellites using robotics

This invention is a method and supporting apparatus for autonomously capturing, servicing and de-orbiting a free-flying spacecraft, such as a satellite, using robotics. The capture of the spacecraft includes the steps of optically seeking and ranging the satellite using LIDAR; and matching tumble rates, rendezvousing and berthing with the satellite. Servicing of the spacecraft may be done using supervised autonomy, which is allowing a robot to execute a sequence of instructions without intervention from a remote human-occupied location. These instructions may be packaged at the remote station in a script and uplinked to the robot for execution upon remote command giving authority to proceed. Alternately, the instructions may be generated by Artificial Intelligence (AI) logic onboard the robot. In either case, the remote operator maintains the ability to abort an instruction or script at any time, as well as the ability to intervene using manual override to teleoperate the robot.In one embodiment, a vehicle used for carrying out the method of this invention comprises an ejection module, which includes the robot, and a de-orbit module. Once servicing is completed by the robot, the ejection module separates from the de-orbit module, leaving the de-orbit module attached to the satellite for de-orbiting the same at a future time. Upon separation, the ejection module can either de-orbit itself or rendezvous with another satellite for servicing. The ability to de-orbit a spacecraft further allows the opportunity to direct the landing of the spent satellite in a safe location away from population centers, such as the ocean.

Cepollina, Frank J.↗

NASA NTRS2009

NASA Tech Briefs, January 2009

Tech Briefs are short announcements of innovations originating from research and development activities of the National Aeronautics and Space Administration. They emphasize information considered likely to be transferable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. Topics covered include: The Radio Frequency Health Node Wireless Sensor System; Effects of Temperature on Polymer/Carbon Chemical Sensors; Small CO2 Sensors Operate at Lower Temperature; Tele-Supervised Adaptive Ocean Sensor Fleet; Synthesis of Submillimeter Radiation for Spectroscopy; 100-GHz Phase Switch/Mixer Containing a Slot-Line Transition; Generating Ka-Band Signals Using an X-Band Vector Modulator; SiC Optically Modulated Field-Effect Transistor; Submillimeter-Wave Amplifier Module with Integrated Waveguide Transitions; Metrology System for a Large, Somewhat Flexible Telescope; Economical Implementation of a Filter Engine in an FPGA; Improved Joining of Metal Components to Composite Structures; Machined Titanium Heat-Pipe Wick Structure; Gadolinia-Doped Ceria Cathodes for Electrolysis of CO2; Utilizing Ocean Thermal Energy in a Submarine Robot; Fuel-Cell Power Systems Incorporating Mg-Based H2 Generators; Alternative OTEC Scheme for a Submarine Robot; Sensitive, Rapid Detection of Bacterial Spores; Adenosine Monophosphate-Based Detection of Bacterial Spores; Silicon Microleaks for Inlets of Mass Spectrometers; CGH Figure Testing of Aspherical Mirrors in Cold Vacuums; Series-Coupled Pairs of Silica Microresonators; Precise Stabilization of the Optical Frequency of WGMRs; Formation Flying of Components of a Large Space Telescope; Laser Metrology Heterodyne Phase-Locked Loop; Spatial Modulation Improves Performance in CTIS; High-Performance Algorithm for Solving the Diagnosis Problem; Truncation Depth Rule-of-Thumb for Convolutional Codes; Efficient Method for Optimizing Placement of Sensors.

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NASA NTRS2009

Ares I First Stage Booster Deceleration System: An Overview

In 2005, the Congressional NASA Authorization Act enacted a new space exploration program, the "Vision for Space Exploratien". The Constellation Program was formed to oversee the implementation of this new mission. With an intent not simply to support the International Space Station, but to build a permanent outpost on the Moon and then travel on to explore ever more distant terrains, the Constellation Program is supervising the development of a brand new fleet of launch vehicles, the Ares. The Ares lineup will include two new launch vehicles: the Ares I Crew Launch Vehicle and the Ares V Cargo Launch Vehicle. A crew exploration vehicle, Orion, will be launched on the Ares I. It will be capable of docking with the Space Station, the lunar lander, Altair, and the Earth Departure Stage of Ares V. The Ares V will be capable of lifting both large-scale hardware and the Altair into space. The Ares First Stage Team is tasked with developing the propulsion system necessary to liftoff from the Earth and loft the entire Ares vehicle stack toward low Earth orbit. The Ares I First Stage booster is a 12-foot diameter, five-segment, reusable solid rocket booster derived from the Space Shuttle's four segment reusable solid rocket booster (SRB). It is separated from the Upper Stage through the use of a Deceleration Subsystem (DSS). Booster Tumble Motors are used to induce the pitch tumble following separation from the Upper Stage. The spent Ares I booster must be recoverable using a parachute deceleration system similar to that of the Shuttle SRB heritage system. Since Ares I is much heavier and reenters the Earth's atmosphere from a higher altitude at a much higher velocity than the SRB, all of the parachutes must be redesigned to reliably meet the operational requisites of the new launch vehicles. This paper presents an overview of this new booster deceleration system. It includes comprehensive detail of the parachute deceleration system, its design and deployment sequences, including how and why it is being developed, the requirements it must meet, and the testing involved in its implementation.

King, Ron↗

NASA NTRS2008

Tool Support for Parametric Analysis of Large Software Simulation Systems

The analysis of large and complex parameterized software systems, e.g., systems simulation in aerospace, is very complicated and time-consuming due to the large parameter space, and the complex, highly coupled nonlinear nature of the different system components. Thus, such systems are generally validated only in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. We have addressed the factors deterring such an analysis with a tool to support envelope assessment: we utilize a combination of advanced Monte Carlo generation with n-factor combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. Additional test-cases, automatically generated from models (e.g., UML, Simulink, Stateflow) improve the coverage. The distributed test runs of the software system produce vast amounts of data, making manual analysis impossible. Our tool automatically analyzes the generated data through a combination of unsupervised Bayesian clustering techniques (AutoBayes) and supervised learning of critical parameter ranges using the treatment learner TAR3. The tool has been developed around the Trick simulation environment, which is widely used within NASA. We will present this tool with a GN&C (Guidance, Navigation and Control) simulation of a small satellite system.

Schumann, Johann↗

NASA NTRS2009

Complexity for Survival of Living Systems

A logical connection between the survivability of living systems and the complexity of their behavior (equivalently, mental complexity) has been established. This connection is an important intermediate result of continuing research on mathematical models that could constitute a unified representation of the evolution of both living and non-living systems. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the two most relevant being Characteristics of Dynamics of Intelligent Systems (NPO- 21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; and Self-Supervised Dynamical Systems (NPO- 30634) NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72. As used here, living systems is synonymous with active systems and intelligent systems. The quoted terms can signify artificial agents (e.g., suitably programmed computers) or natural biological systems ranging from single-cell organisms at one extreme to the whole of human society at the other extreme. One of the requirements that must be satisfied in mathematical modeling of living systems is reconciliation of evolution of life with the second law of thermodynamics. In the approach followed in this research, this reconciliation is effected by means of a model, inspired partly by quantum mechanics, in which the quantum potential is replaced with an information potential. The model captures the most fundamental property of life - the ability to evolve from disorder to order without any external interference. The model incorporates the equations of classical dynamics, including Newton s equations of motion and equations for random components caused by uncertainties in initial conditions and by Langevin forces. The equations of classical dynamics are coupled with corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces that are gradients of the information potential, which, in turn, is a function of the probability densities. The probability densities are associated with mental images both self-image and nonself images (images of external objects that can include other agents). The evolution of the probability densities represents mental dynamics. Then the interaction between the physical and metal aspects of behavior is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. The interaction of a system with its self and nonself images affords unlimited capacity for increase of complexity. There is a biological basis for this model of mental dynamics in the discovery of mirror neurons that learn by imitation. The levels of complexity attained by use of this model match those observed in living systems. To establish a mechanism for increasing the complexity of dynamics of an active system, the model enables exploitation of a chain of reflections exemplified by questions of the form, "What do you think that I think that you think...?" Mathematically, each level of reflection is represented in the form of an attractor performing the corresponding level of abstraction with more details removed from higher levels. The model can be used to describe the behaviors, not only of biological systems, but also of ecological, social, and economics ones.

Zak, Michail↗

NASA NTRS2009

NASA Tech Briefs, August 2009

Topics covered include: Aligning a Receiving Antenna Array to Reduce Interference; Collecting Ground Samples for Balloon-Borne Instruments; Tethered Pyrotechnic Apparatus for Acquiring a Ground Sample; Enhanced Video-Oculography System; Joint Carrier-Phase Synchronization and LDPC Decoding; Dual-Polarization, Sideband-Separating, Balanced Receiver for 1.5 THz Modular Battery Charge Controller; Efficient Multiplexer FPGA Block Structures Based on G4FETs; VLSI Microsystem for Rapid Bioinformatic Pattern Recognition; Low-Noise Amplifier for 100 to 180 GHz; Improved Fabrication of Ceramic Matrix Composite/Foam Core Integrated Structures; Inert Welding/Brazing Gas Filters and Dryers; Fabricating Copper Nanotubes by Electrodeposition; Reducing Aerodynamic Drag on Empty Open Cargo Vehicles; Rotary Percussive Auto-Gopher for Deep Drilling and Sampling; More About Reconfigurable Exploratory Robotic Vehicles; Thermostatic Valves Containing Silicone-Oil Actuators; Improving Heat Flux Performance of Flat Surface in Spray-Cooling Systems; Treating Fibrous Insulation to Reduce Thermal Conductivity; Silica-Aerogel Composites Opacified with La(sub0.7)Sr(sub0.3)MnO3; Cyclic Oxidation Behavior of CuCrAl Cold-Sprayed Coatings for Reusable Launch Vehicles; Ceramic Fiber Structures for Cryogenic Load-Bearing Applications; Elastomer Reinforced with Carbon Nanotubes; Biologically Inspired Purification and Dispersion of SWCNTs; A Technique for Adjusting Eigenfrequencies of WGM Resonators; Low-Pressure, Field-Ionizing Mass Spectrometer; Modifying Operating Cycles to Increase Stability in a LITS; Chamber for Simulating Martian and Terrestrial Environments; Algorithm for Detecting a Bright Spot in an Image; Extreme Programming: Maestro Style; Adaptive Behavior for Mobile Robots; Protocol for Communication Networking for Formation Flying; Planning Complex Sequences Using Compressed Representations; and Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors.

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NASA NTRS2009

Parametric Analysis of a Hover Test Vehicle using Advanced Test Generation and Data Analysis

Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.

Gundy-Burlet, Karen↗

NASA NTRS2009

Medical Devices Assess, Treat Balance Disorders

You may have heard the phrase as difficult as walking and chewing gum as a joking way of referring to something that is not difficult at all. Just walking, however, is not all that simple physiologically speaking. Even standing upright is an undertaking requiring the complex cooperation of multiple motor and sensory systems including vision, the inner ear, somatosensation (sensation from the skin), and proprioception (the sense of the body s parts in relation to each other). The compromised performance of any of these elements can lead to a balance disorder, which in some form affects nearly half of Americans at least once in their lifetimes, from the elderly, to those with neurological or vestibular (inner ear) dysfunction, to athletes with musculoskeletal injuries, to astronauts returning from space. Readjusting to Earth s gravity has a significant impact on an astronaut s ability to balance, a result of the brain switching to a different "model" for interpreting sensory input in normal gravity versus weightlessness. While acclimating, astronauts can experience headaches, motion sickness, and problems with perception. To help ease the transition and study the effects of weightlessness on the body, NASA has conducted many investigations into post-flight balance control, realizing this research can help treat patients with balance disorders on Earth as well. In the 1960s, the NASA-sponsored Man Vehicle Laboratory at the Massachusetts Institute of Technology (MIT) studied the effects of prolonged space flight on astronauts. The lab s work intrigued MIT doctoral candidate Lewis Nashner, who began conducting NASA-funded research on human movement and balance under the supervision of Dr. Larry Young in the MIT Department of Aeronautics and Astronautics. In 1982, Nashner s work resulted in a noninvasive clinical technique for assessing the cooperative systems that allow the body to balance, commonly referred to as computerized dynamic posturography (CDP). CDP employs a series of dynamic protocols to isolate and assess balance function deficiencies. The technology was based on Nashner s novel, engineering-inspired concept of balance as an adaptable collaboration between multiple sensory and motor systems. CDP proved useful not only for examining astronauts, but for anyone suffering from balance problems. Today, CDP is the standard medical tool for objectively evaluating balance control.

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NASA NTRS2009

Rover Attitude and Pointing System Simulation Testbed

The MER (Mars Exploration Rover) Attitude and Pointing System Simulation Testbed Environment (RAPSSTER) provides a simulation platform used for the development and test of GNC (guidance, navigation, and control) flight algorithm designs for the Mars rovers, which was specifically tailored to the MERs, but has since been used in the development of rover algorithms for the Mars Science Laboratory (MSL) as well. The software provides an integrated simulation and software testbed environment for the development of Mars rover attitude and pointing flight software. It provides an environment that is able to run the MER GNC flight software directly (as opposed to running an algorithmic model of the MER GNC flight code). This improves simulation fidelity and confidence in the results. Further more, the simulation environment allows the user to single step through its execution, pausing, and restarting at will. The system also provides for the introduction of simulated faults specific to Mars rover environments that cannot be replicated in other testbed platforms, to stress test the GNC flight algorithms under examination. The software provides facilities to do these stress tests in ways that cannot be done in the real-time flight system testbeds, such as time-jumping (both forwards and backwards), and introduction of simulated actuator faults that would be difficult, expensive, and/or destructive to implement in the real-time testbeds. Actual flight-quality codes can be incorporated back into the development-test suite of GNC developers, closing the loop between the GNC developers and the flight software developers. The software provides fully automated scripting, allowing multiple tests to be run with varying parameters, without human supervision.

Vanelli, Charles A.↗

NASA NTRS2007

Multiple-Agent Air/Ground Autonomous Exploration Systems

Autonomous systems of multiple-agent air/ground robotic units for exploration of the surfaces of remote planets are undergoing development. Modified versions of these systems could be used on Earth to perform tasks in environments dangerous or inaccessible to humans: examples of tasks could include scientific exploration of remote regions of Antarctica, removal of land mines, cleanup of hazardous chemicals, and military reconnaissance. A basic system according to this concept (see figure) would include a unit, suspended by a balloon or a blimp, that would be in radio communication with multiple robotic ground vehicles (rovers) equipped with video cameras and possibly other sensors for scientific exploration. The airborne unit would be free-floating, controlled by thrusters, or tethered either to one of the rovers or to a stationary object in or on the ground. Each rover would contain a semi-autonomous control system for maneuvering and would function under the supervision of a control system in the airborne unit. The rover maneuvering control system would utilize imagery from the onboard camera to navigate around obstacles. Avoidance of obstacles would also be aided by readout from an onboard (e.g., ultrasonic) sensor. Together, the rover and airborne control systems would constitute an overarching closed-loop control system to coordinate scientific exploration by the rovers.

Fink, Wolfgang↗

NASA NTRS2009

Method and associated apparatus for capturing, servicing, and de-orbiting earth satellites using robotics

Cepollina, Frank J.↗

NASA NTRS2009

Method and associated apparatus for capturing, servicing, and de-orbiting earth satellites using robotics

Cepollina, Frank J.↗

NASA NTRS2010

Confidence-Based Feature Acquisition

Confidence-based Feature Acquisition (CFA) is a novel, supervised learning method for acquiring missing feature values when there is missing data at both training (learning) and test (deployment) time. To train a machine learning classifier, data is encoded with a series of input features describing each item. In some applications, the training data may have missing values for some of the features, which can be acquired at a given cost. A relevant JPL example is that of the Mars rover exploration in which the features are obtained from a variety of different instruments, with different power consumption and integration time costs. The challenge is to decide which features will lead to increased classification performance and are therefore worth acquiring (paying the cost). To solve this problem, CFA, which is made up of two algorithms (CFA-train and CFA-predict), has been designed to greedily minimize total acquisition cost (during training and testing) while aiming for a specific accuracy level (specified as a confidence threshold). With this method, it is assumed that there is a nonempty subset of features that are free; that is, every instance in the data set includes these features initially for zero cost. It is also assumed that the feature acquisition (FA) cost associated with each feature is known in advance, and that the FA cost for a given feature is the same for all instances. Finally, CFA requires that the base-level classifiers produce not only a classification, but also a confidence (or posterior probability).

Wagstaff, Kiri L.↗

NASA NTRS2007

Automated Recognition of 3D Features in GPIR Images

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Park, Han↗

NASA NTRS2007

Automated Diagnosis and Control of Complex Systems

Livingstone2 is a reusable, artificial intelligence (AI) software system designed to assist spacecraft, life support systems, chemical plants, or other complex systems by operating with minimal human supervision, even in the face of hardware failures or unexpected events. The software diagnoses the current state of the spacecraft or other system, and recommends commands or repair actions that will allow the system to continue operation. Livingstone2 is an enhancement of the Livingstone diagnosis system that was flight-tested onboard the Deep Space One spacecraft in 1999. This version tracks multiple diagnostic hypotheses, rather than just a single hypothesis as in the previous version. It is also able to revise diagnostic decisions made in the past when additional observations become available. In such cases, Livingstone might arrive at an incorrect hypothesis. Re-architecting and re-implementing the system in C++ has increased performance. Usability has been improved by creating a set of development tools that is closely integrated with the Livingstone2 engine. In addition to the core diagnosis engine, Livingstone2 includes a compiler that translates diagnostic models written in a Java-like language into Livingstone2's language, and a broad set of graphical tools for model development.

Kurien, James↗

NASA NTRS2009

Comparison of GOES Cloud Classification Algorithms Employing Explicit and Implicit Physics

Cloud-type classification based on multispectral satellite imagery data has been widely researched and demonstrated to be useful for distinguishing a variety of classes using a wide range of methods. The research described here is a comparison of the classifier output from two very different algorithms applied to Geostationary Operational Environmental Satellite (GOES) data over the course of one year. The first algorithm employs spectral channel thresholding and additional physically based tests. The second algorithm was developed through a supervised learning method with characteristic features of expertly labeled image samples used as training data for a 1-nearest-neighbor classification. The latter's ability to identify classes is also based in physics, but those relationships are embedded implicitly within the algorithm. A pixel-to-pixel comparison analysis was done for hourly daytime scenes within a region in the northeastern Pacific Ocean. Considerable agreement was found in this analysis, with many of the mismatches or disagreements providing insight to the strengths and limitations of each classifier. Depending upon user needs, a rule-based or other postprocessing system that combines the output from the two algorithms could provide the most reliable cloud-type classification.

EXPLICIT PHYSICS ALGORITHMS↗

NASA NTRS2010

Modeling Common-Sense Decisions in Artificial Intelligence

A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation of the dynamical models in a parameterized form reduces the task of common-sense-based decision making to a solution of the following hetero-associated-memory problem: store a set of m predetermined stochastic processes given by their probability distributions in such a way that when presented with an unexpected change in the form of an input out of the set of M inputs, the coupled motormental dynamics converges to the corresponding one of the m pre-assigned stochastic process, and a sample of this process represents the decision.

Zak, Michail↗

NASA NTRS2006

DCS-Neural-Network Program for Aircraft Control and Testing

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Jorgensen, Charles C.↗