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A novel mathematical framework for the rapid learning of nonlinear mappings and topological transformations is presented. It is based on allowing the neuron's parameters to adapt as a function of learning. This fully recurrent adaptive neuron model (ANM) has been successfully applied to complex nonlinear function approximation problems such as the highly degenerate inverse kinematics problem in robotics.
The functions and facilities of the Deep Space Network, its supporting research and technology and network operations are discussed.
The functions and facilities of the Deep Space Network are considered. Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported.
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The authors present smoothed perturbation analysis (SPA) estimators for the derivative of a number of occupancy-related functions in serial queuing networks with finite buffer spaces. The functions are the average number of customers at a network as seen by an arrival, the probability that a customer is blocked at a particular queue, and the probability that a customer leaves a queue empty. In all three cases, the variable is a parameter of the distribution of service times at one of the queues. The derivative estimators considered are very simple and flexible, and they easily lend themselves to analysis of unbiasedness. Unlike most of the established SPA estimators, the present ones are not based on the computation of hazard rates.
Several energy functions for synthesizing neural networks are tested on 2-D synthetic data and on Landsat-4 Thematic Mapper data. These new energy functions, designed specifically for minimizing misclassification error, in some cases yield significant improvements in classification accuracy over the standard least mean squares energy function. In addition to operating on networks with one output unit per class, a new energy function is tested for binary encoded outputs, which result in smaller network sizes. The Thematic Mapper data (four bands were used) is classified on a single pixel basis, to provide a starting benchmark against which further improvements will be measured. Improvements are underway to make use of both subpixel and superpixel (i.e. contextual or neighborhood) information in tile processing. For single pixel classification, the best neural network result is 78.7 percent, compared with 71.7 percent for a classical nearest neighbor classifier. The 78.7 percent result also improves on several earlier neural network results on this data.
Active RC network is capable of extremely high Q performance with exceptional stability and has independently adjustable zeros and poles. The circuit consists of two integrators and two summers that are interconnected to produce a complete second-order numerator and a second-order denominator.
The use of radial basis function networks for fine pointing NASA's 70-meter deep space network antennas is described and evaluated.
The transmission characteristics of synthesized optical single-pass and double-pass birefringent networks is obtained by measuring network transmission as a function of network temperature. This technique is most useful for testing those birefringent networks whose bandwidths and periods are very small.
Spaceflight effects on gait, balance, & manual motor control have been well studied; some evidence for cognitive deficits. Rodent cortical motor & sensory systems show neural structural alterations with spaceflight. specific Aims: Aim 1-Identify changes in brain structure, function, and network integrity as a function of head down tilt bed rest and spaceflight, and characterize their time course. Aim 2-Specify relationships between structural and functional brain changes and performance and characterize their time course.
Spaceflight effects on gait, balance, & manual motor control have been well studied; some evidence for cognitive deficits. Rodent cortical motor & sensory systems show neural structural alterations with spaceflight. We found extensive changes in behavior, brain structure & brain function following 70 days of HDBR. Specific Aim: Aim 1-Identify changes in brain structure, function, and network integrity as a function of spaceflight and characterize their time course. Aim 2-Specify relationships between structural and functional brain changes and performance and characterize their time course.
While the primary function of the network in a parallel computer is to communicate data between processors, it is often useful if the network can also perform rudimentary calculations. That is, some simple processing ability in the network itself, particularly for performing parallel prefix computations, can reduce both the volume of data being communicated and the computational load on the processors proper. Unfortunately, typical implementations of such networks require a large fraction of the hardware budget, and so combining networks are viewed as being impractical. The FFP Machine has such a combining network, and various characteristics of the machine allow a good deal of simplification in the network design. Despite being simple in construction however, the network relies on many subtle details to work correctly. This paper describes an executable model of the network which will serve several purposes. It provides a complete and detailed description of the network which can substantiate its ability to support necessary functions. It provides an environment in which algorithms to be run on the network can be designed and debugged more easily than they would on physical hardware. Finally, it provides the foundation for exploring the design of the message receiving facility which connects the network to the individual processors.
Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.
This paper discusses the performance of the network control function for the Advanced Communications Technology Satellite (ACTS) very small aperture terminal (VSAT) full mesh network. This includes control of all operational activities such as acquisition, synchronization, timing and rain fade compensation as well as control of all communications activities such as on-demand integrated services (voice, video, and date) connects and disconnects Operations control is provided by an in-band orderwire carried in the baseboard processor (BBP) control burst, the orderwire burst, the reference burst, and the uplink traffic burst. Communication services are provided by demand assigned multiple access (DAMA) protocols. The ACTS implementation of DAMA protocols ensures both on-demand and integrated voice, video and data services. Communications services control is also provided by the in-band orderwire but uses only the reference burst and the uplink traffic burst. The performance of the ACTS network control functions have been successfully tested during on-orbit checkout and in various VSAT networks in day to day operations. This paper discusses the network operations and services control performance.
Network control (or network management) functions are essential for efficient and reliable operation of a network. Some control functions are currently included as part of the Open System Interconnection model. For local area networks, it is widely recognized that there is a need for additional control functions, including fault isolation functions, monitoring functions, and configuration functions. These functions can be implemented in either a central or distributed manner. The Fiber Distributed Data Interface Medium Access Control and Station Management protocols provide an example of distributed implementation. Relative information is presented here in outline form.
To compare the validity of current algorithms that map zenith tropospheric delay to arbitrary elevation angles, 10 different tropospheric mapping functions are used to analyze the current data base of Deep Space Network Mark 3 intercontinental very long baseline interferometric (VLBI) data. This analysis serves as a stringent test because of the high proportion of low-elevation observations necessitated by the extremely long baselines. Postfit delay and delay-rate residuals are examined, as well as the scatter of baseline lengths about the time-linear model that characterizes tectonic motion. Among the functions that utilize surface meteorological data as input parameters, the Lanyi 1984 mapping shows the best performance both for residuals and baselines, through the 1985 Davis function is statistically nearly identical. The next best performance is shown by the recent function of Niell, which is based on an examination of global atmospheric characteristics as a function of season and uses no weather data at the time of the measurements. The Niell function shows a slight improvement in residuals relative to Lanyi, but also an increase in baseline scatter that is significant for the California-Spain baseline. Two variants of the Chao mapping function, as well as the Chao tables used with the interpolation algorithm employed in the Orbit Determination Program software, show substandard behavior for both VLBI residuals and baseline scatter. The length of the California-Australia baseline (10,600 km) in the VLBI solution can vary by as much as 5 to 10 cm for the 10 mapping functions.
Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.
Onboard baseband processing architectures for future satellite broadband integrated services digital networks (B-ISDN's) are addressed. To assess the feasibility of implementing satellite B-ISDN services, critical design issues, such as B-ISDN traffic characteristics, transmission link design, and a trade-off between onboard circuit and fast packet switching, are analyzed. Examples of the two types of switching mechanisms and potential onboard network control functions are presented. A sample network architecture is also included to illustrate a potential onboard processing system.