Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.     

A global inventory of nitric oxide emissions from soils

Over 60 published papers reporting field measurements of emissions of nitric oxide (NO) from soil are reviewed, and over 100 annual estimates of NO emissions were made for various types of ecosystems, including agricultural fields. These data were stratified by biome and the mean of each stratum was multiplied by an estimate of the biome area. A few strata were identified as clearly having low NO emissions: montane forests, swamps and marshes, tundra, and temperate forests that are not heavily affected by N deposition. The largest emissions were observed in tropical savanna/woodland, chaparral, and cultivated agriculture, but variation in NO emissions within these strata was also large. Although the stratification scheme fails to partition this within-stratum variation, it does clearly identify these biomes as globally important sources of NO and as areas where more research is needed to investigate within-biome variation in NO emissions. It is too early to tell whether differences in NO emissions between temperate and tropical agriculture are significant, but it is clear that agriculture is an important source of NO and that management practices affect NO emissions. The best current estimate of the global soil source of NO is 21 Tg N yr^-1. Adsorption of NO_x onto plant canopy surfaces may reduce emissions to the atmosphere to as low as 13 Tg N yr^-1, although the absorption effect is probably smaller than this. An error term for the global estimate is difficult to determine, but it is at least ±4 and perhaps as large at ±10 Tg N yr^-1. Hence, only modest progress has been made in narrowing uncertainties in the estimate of the global soil source of NO, although some published lower estimates appear unlikely. This inventory reconfirms that the soil source of NO is similar in magnitude to fossil fuel emissions of NO_x. Further narrowing of the uncertainty of the estimate of global soil NO emissions will require more sophisticated and carefully chosen stratification schemes to address variation within biomes based on soil fertility, soil texture, climate, and management and will require linking this type of inventory and stratification with mechanistic models.     

Particle launch to 19 km/s for micro-meteoroid simulation using enhanced three-stage light gas gun hypervelocity launcher techniques

Particle launch experiments were performed to study application of the enhanced hypervelocity launcher (EHVL), i.e. the third-stage addition to the two-stage gun, for launching micron to millimeter sized particulates at velocities unobtainable with a standard two-stage light gas gun launch. Three types of particles or fliers were tested along with several barrel designs. For micron scale particles fine-grain polycrystalline ceramics were impacted and fractured, launching particulate clouds at velocities of 15 km/s. Multiple titanium particles 400 μm diameter embedded in plastic were “shotgun” launched to velocities of 10 km/s. Flier plates of 3 mm diameter by 1 mm thick Ti6Al4V were launched to 19 km/s. All experiments used a second-stage projectile with graded density facing impacting a flier in an impact generated acceleration reservoir. This paper describes the modification and adaptation of the Sandia EHVL to provide micrometeoroid simulation capabilities.     

Gas turbine disk-blade attachment crack

Evaluation of a gas turbine disk revealed a crack in the blade attachment area. The subsequent effort to understand the origin of this crack led to a series of analyses that included computing the stresses on the attachment, characterization of fatigue crack growth, and a model for fretting fatigue crack growth. These elements were brought together to simulate the conditions that led to the cracking. It is concluded that the crack was probably caused by fretting fatigue induced by the stresses related to normal takeoff and landing cycles and exacerbated by aircraft maneuvers, and that short periods of blade resonance may have contributed to the cracking. If material had not been removed from the attachment surface of the disk by service-induced wear, it is likely more cracks would have been found.     

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km² in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m³/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.     

Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data

Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62).     

The majority theorem of centralized multiple BAMs networks

A method for modeling the learning of belief combination in evidential reasoning using a neural network is presented. A centralized network composed of multiple bidirectional associative memories (BAMs) sharing a single output array of neurons is proposed to process the uncertainty management of many pieces of evidence simultaneously. The convergence properties of the multi-BAM network are proved. The combination process of evidence is considered as a resonant process through the multi-BAM networks. Most important of all, a majority rule of decision making in presentation of multiple evidence is also found by the study of signal-noise-ratio (SNR) of the multi-BAM network. Some simulation examples are given. The result is coherent with the intuition of reasoning.     

Characterizing distributed shared memory performance: a case studyof the Convex SPP1000

In a distributed shared memory (DSM) multiprocessor, the processors cooperate in solving a parallel application by accessing the shared memory. The latency of a memory access depends on several factors, including the distance to the nearest valid data copy, data sharing conditions, and traffic of other processors. To provide a better understanding of DSM performance and to support application tuning and compiler development for DSM systems, this paper extends microbenchmarking techniques to characterize the important aspects of a DSM system. We present an experiment-based methodology for characterizing the memory, communication, scheduling, and synchronization performance, and apply it to the Convex SPP1000. We present carefully designed microbenchmarks to characterize the performance of the local and remote memory, producer-consumer communication involving two or more processors, and the effects on performance when multiple processors contend for utilization of the distributed memory and the interconnection network