Hybrid neural network and fuzzy logic approaches for rendezvous and capture in space

The nonlinear behavior of many practical systems and unavailability of quantitative data regarding the input-output relations makes the analytical modeling of these systems very difficult. On the other hand, approximate reasoning-based controllers which do not require analytical models have demonstrated a number of successful applications such as the subway system in the city of Sendai. These applications have mainly concentrated on emulating the performance of a skilled human operator in the form of linguistic rules. However, the process of learning and tuning the control rules to achieve the desired performance remains a difficult task. Fuzzy Logic Control is based on fuzzy set theory. A fuzzy set is an extension of a crisp set. Crisp sets only allow full membership or no membership at all, whereas fuzzy sets allow partial membership. In other words, an element may partially belong to a set

Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing

Constraints on Axions from a Theoretical Model of Spatially-Extended Gamma-Ray Emission from Neutron Stars

Axions are hypothetical particles proposed to solve the strong CP problem in QCD and may constitute a significant fraction of the dark matter in the Universe. Axions are expected to be produced in neutron stars and subsequently decay, producing gamma-rays detectable by the Fermi Large Area Telescope (Fermi-LAT). Considering that light QCD axions, as opposed to axions $>1$eV, may travel a long range before they decay into gamma rays, neutron stars may appear as a spatially-extended source of gamma rays. We extend our previous search for gamma rays from axions, based on a point source model, to consider the neutron star as an extended source of gamma rays. The extended consideration of neutron stars' leads to higher sensitivity to searches for axions, as it will be shown. We investigate the spatial emission of gamma rays using phenomenological models of neutron star axion emission. We present models including the fundamental astrophysics and relativistic, extended gamma-ray emission from axions around neutron stars. A Monte Carlo simulation of the LAT gives us an expectation for the extended angular profile and spectrum. For a source of $\simeq$ 100 pc, we predict a mean angular spread of $\simeq 2^\circ$ with gamma-ray energies in the range 10-200 MeV. We demonstrate the feasibility of setting more stringent limits for axions in this mass range, excluding a range not probed by observations before. We consider projected sensitivities for mass limits on axions from J0108-1431. Based on the extended angular profile of the source, the expected sensitivity of the 95\% CL upper limit on the axion mass from J0108-1431 is $\lesssim$10 meV. The limit based on 7.9 years of Fermi-LAT data is 0.76 meV for an inner temperature of the neutron star of 20 MeV

A Comparative Study of Rehearsal and Loci Methods in Learning Vocabulary in EFL Context

Effective learning in foreign language settings depends on acquiring a large number of vocabularies. This study intended to compare two vocabulary learning methods known as loci and rehearsal methods to find out which one leads to better retention and recalling of words. Employing a quasi-experimental research, 80 learners from two intact classes in Islamic Azad University, Osku Branch, Iran, were randomly selected as the experimental and control groups. For the purpose of vocabulary learning, the experimental group trained in loci method while rehearsal strategy training was used in the control group.  At the end of each session of the treatment, multiple-choice vocabulary tests were used to measure whether the participants can recall the lexical items from their short-term memory. A delayed multiple-choice posttest of vocabulary was also used in order to compare vocabulary learning among two groups four weeks after the treatment. Implementing Independent Samples t-test, the results indicated that experimental group was better than control group in retention and recalling of lexical items in immediate posttest. It was also found that the loci method was more effective than rehearsal in permanency of lexical items in long term memory. Syllabus designers and textbook writers can consider different learning strategies in designing vocabulary books by taking the learners’ proficiency level into account

NASA/ARC proposed training in intelligent control

Viewgraphs on NASA Ames Research Center proposed training in intelligent control was presented. Topics covered include: fuzzy logic control; neural networks in control; artificial intelligence in control; hybrid approaches; hands on experience; and fuzzy controllers

Variability and stability of tuber yield of Jerusalem artichoke (Helianthus tuberosus L.)

The objective of this paper was to estimate the genetic and ecological variation as well as the stability of tuber yield per plant, tuber number per plant and tuber size of Jerusalem artichoke based on the results of a variety trial carried out with 20 different Jerusalem artichoke varieties during the period of 1994-2000. Significant genetic as well as ecological variation was observed for all of the traits studied. The most promising varieties showing high tuber yield combined with high yield stability were 'BT-4', 'Violet Rennes' and 'UKR 4/82'. It is encouraging that the highest yielding varieties exhibited a rather stable performance over environments

Fuzzy and neural control

Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning