Adding a receding horizon to Locally Weighted Regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum.

The importance of neighbourhood size in self organising systems

In recent times, the analysis of SOM (self-organising map) performance has concentrated on optimising the gain decay, rather than the size, form and decay of the neighbourhood function. We propose that the size, form and decay of region size plays a much more significant role in the learning, and especially in the development, of topographic feature maps. In this paper, a biologically-derived SOM model is presented. This model is able to select a single winning neuron and to form Gaussian outputs about this winner, without the need for a meta-level decision-making structure to artificially select a winner and fit a Gaussian output to that winner. Using this model, some fundamental characteristics of the relationship between neighbourhood size and SOM output states are demonstrated.

Beyond the topological map : developing alternate mappings in self organisation

The self organising map is a well established unsupervised
learning technique which is able to form sophisticated representations of an input data set. However, conventional Self Organising Map (SOM) algorithms are limited to the production of topological maps — that is, maps where distance between points on the map have a direct relationship to the Euclidean distance between the training vectors corresponding to those points.

It would be desirable to be able to create maps which form clusters on primitive attributes other than Euclidean distance; for example, clusters based upon orientation or shape. Such maps could provide a novel approach to pattern recognition tasks by providing a new method to associate groups of data.

In this paper, it is shown that the type of map produced by SOM algorithms is a direct consequence of the lateral connection strategy employed. Given this knowledge, a technique is required to establish the feasability of using an alternative lateral connection strategy. Such a technique is presented. Using this technique, it is possible to rule out lateral connection strategies that will not produce output states useful to the organisation process. This technique is demonstrated using conventional Laplacian interconnection as well as a number of novel interconnection strategies.

Wavelength monitoring with semiconductor laser amplifiers

The semiconductor laser diodes that are typically used in applications of optical communications, when working as amplifiers, present under certain conditions optical bistability, which is characterized by abruptly switching between two different output states and an associated hysteresis cycle. This bistable behavior is strongly dependent on the frequency detuning between the frequency of the external optical signal that is injected into the semiconductor laser amplifier and its own emission frequency. This means that small changes in the wavelength of an optical signal applied to a laser amplifier causes relevant changes in the characteristics of its transfer function in terms of the power requirements to achieve bistability and the width of the hysteresis. This strong dependence in the working characteristics of semiconductor laser amplifiers on frequency detuning suggest the use of this kind of devices in optical sensing applications for optical communications, such as the detection of shifts in the emission wavelength of a laser, or detect possible interference between adjacent channels in DWDM (Dense Wavelength Division Multiplexing) optical communication networks

Potential output in a rapidly developing economy: the case of China and a comparison with the United States and the European Union

The authors use a growth accounting framework to examine growth of the rapidly developing Chinese economy. Their findings support the view that, although feasible in the intermediate term, China's recent pattern of extensive growth is not sustainable in the long run. The authors believe that China will be able to sustain a growth rate of 8 to 9 percent for an extended period if it moves from extensive to intensive growth. They next compare potential growth in China with historical developments in the United States and the European Union. They discuss the differences in production structure and level of development across the three economies that may explain the countries' varied intermediate-term growth prospects. Finally, the authors provide an analysis of "green" gross domestic product and the role of natural resources in China's growth. © 2009, The Federal Reserve Bank of St. Louis.

Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States

Many macroeconomic series, such as U.S. real output growth, are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS specification used in the comparison uses a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way to exploit monthly data compared with alternative methods.

The effect of OPEC oil pricing on output, prices, and exchange rates in the United States and other industrial countries /

Mode of access: Internet.

Output of factories processing farm food products in the United States, 1909-1958 /

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Stochastic output feedback model predictive control

This paper addresses the issue of output feedback model predictive control for linear systems with input constraints and stochastic disturbances. We show that the optimal policy uses the Kalman filter for state estimation, but the resultant state estimates are not utilized in a certainty equivalence control law

Do Measurement Errors in GDP Announcements Cause Output Fluctuations?

This thesis studies empirically whether measurement errors in aggregate production statistics affect sentiment and future output. Initial announcements of aggregate production are subject to measurement error, because many of the data required to compile the statistics are produced with a lag. This measurement error can be gauged as the difference between the latest revised statistic and its initial announcement. Assuming aggregate production statistics help forecast future aggregate production, these measurement errors are expected to affect macroeconomic forecasts. Assuming agents’ macroeconomic forecasts affect their production choices, these measurement errors should affect future output through sentiment. This thesis is primarily empirical, so the theoretical basis, strategic complementarity, is discussed quite briefly. However, it is a model in which higher aggregate production increases each agent’s incentive to produce. In this circumstance a statistical announcement which suggests aggregate production is high would increase each agent’s incentive to produce, thus resulting in higher aggregate production. In this way the existence of strategic complementarity provides the theoretical basis for output fluctuations caused by measurement mistakes in aggregate production statistics. Previous empirical studies suggest that measurement errors in gross national product affect future aggregate production in the United States. Additionally it has been demonstrated that measurement errors in the Index of Leading Indicators affect forecasts by professional economists as well as future industrial production in the United States. This thesis aims to verify the applicability of these findings to other countries, as well as study the link between measurement errors in gross domestic product and sentiment. This thesis explores the relationship between measurement errors in gross domestic production and sentiment and future output. Professional forecasts and consumer sentiment in the United States and Finland, as well as producer sentiment in Finland, are used as the measures of sentiment. Using statistical techniques it is found that measurement errors in gross domestic product affect forecasts and producer sentiment. The effect on consumer sentiment is ambiguous. The relationship between measurement errors and future output is explored using data from Finland, United States, United Kingdom, New Zealand and Sweden. It is found that measurement errors have affected aggregate production or investment in Finland, United States, United Kingdom and Sweden. Specifically, it was found that overly optimistic statistics announcements are associated with higher output and vice versa.

Entanglement and nonclassicality for multimode radiation-field states

Nonclassicality in the sense of quantum optics is a prerequisite for entanglement in multimode radiation states. In this work we bring out the possibilities of passing from the former to the latter, via action of classicality preserving systems like beam splitters, in a transparent manner. For single-mode states, a complete description of nonclassicality is available via the classical theory of moments, as a set of necessary and sufficient conditions on the photon number distribution. We show that when the mode is coupled to an ancilla in any coherent state, and the system is then acted upon by a beam splitter, these conditions turn exactly into signatures of negativity under partial transpose (NPT) entanglement of the output state. Since the classical moment problem does not generalize to two or more modes, we turn in these cases to other familiar sufficient but not necessary conditions for nonclassicality, namely the Mandel parameter criterion and its extensions. We generalize the Mandel matrix from one-mode states to the two-mode situation, leading to a natural classification of states with varying levels of nonclassicality. For two-mode states we present a single test that can, if successful, simultaneously show nonclassicality as well as NPT entanglement. We also develop a test for NPT entanglement after beam-splitter action on a nonclassical state, tracing carefully the way in which it goes beyond the Mandel nonclassicality test. The result of three-mode beam-splitter action after coupling to an ancilla in the ground state is treated in the same spirit. The concept of genuine tripartite entanglement, and scalar measures of nonclassicality at the Mandel level for two-mode systems, are discussed. Numerous examples illustrating all these concepts are presented.

The economic importance of marine angler expenditures in the United States.

In 1998, the National Marine Fisheries Service (NMFS) began a series of marine angler expenditure surveys in the coastal regions of the United States (U.S.) to evaluate marine recreational fishing expenditures and the financial impacts of these expenditures in each region and the U.S. as a whole. In this report, we use the previously estimated expenditure estimates to assess the total financial impact of anglers’ saltwater expenditures. Estimates are provided for sales, income, employment, and tax impacts for each coastal state in the U.S. Aggregate estimates are also provided for the entire U.S., excluding Alaska, Hawaii, and Texas. Direct, indirect, and induced effects associated with resident and non-resident angler expenditures were estimated using a regional input-output modeling system called IMPLAN Pro. Nationwide, recreational saltwater fishing generated over $30.5 billion in sales in 2000, nearly $12.0 billion in income, and supported nearly 350,000 jobs. Approximately 89 cents of every dollar spent by saltwater anglers was estimated to remain within the U.S. economy. At the state level, many of the goods anglers purchased were imports, and, as such, as little as 44 cents of every dollar stayed in Rhode Island and as much as 80 cents of every dollar stayed in Georgia. In the Northeast, the highest impacts were generated in New Jersey, even though recreational fishing expenditures in Massachusetts and Maryland were considerably higher. In the Southeast, the highest impacts were generated in Florida, and on the Pacific Coast, the highest impacts were generated in California. Expenditures on boat maintenance/expenses generated more impacts than any other expenditure category in the U.S. Expenditures on rods and reels was the single most important expense category in terms of generating impacts in most of the Northeast states. Expenditures on boat expenses generated the highest in most Southeast states, and expenditures for boat accessories produced the highest impacts in most Pacific Coast states.(PDF file contains 184 pages.)

The annual course of precipitation over much of the United States: observed versus GCM simulation

General Circulation Models (GCMs) may be useful in estimating the ecological impacts of global climatic change. We analyzed seasonal weather patterns over the conterminous United States and determined that regional patterns of rainfall seasonality appear to control the distributions of the Nation's major biomes. These regional patterns were compared to the output from three GCMs for validation. The models appear to simulate the appropriate seasonal climates in the northern tier of states. However, the spatial extent of these regions is distorted. None of the models accurately portrayed rainfall seasonalities in the southern tier of states, where biomes are primarily influenced by the Bermuda High.

Semiempirical down-scaling of GCM output to the local scale for temperature, precipitation, and runoff

EXTRACT (SEE PDF FOR FULL ABSTRACT): An empirically derived multiple linear regression model is used to relate a local-scale dependent variable (either temperature, precipitation, or surface runoff) measured at individual gauging stations to six large-scale independent variables (temperature, precipitation, surface runoff, height to the 500-mbar pressure surface, and the zonal and meridional gradient across this surface). ...The area investigated is the western United States. ... The calibration data set is from 1948 through 1988 and includes data from 268 joint temperature and precipitation stations, 152 streamflow stations (which are converted to runoff data), and 24 gridded 500-mbar pressure height nodes.