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Learning Capability (learning + capability)

Distribution by Scientific Domains

Engineering	50%
Information Science and Computing	30%
Business, Economics, Finance and Accounting	20%

Selected Abstracts

Learning in a hierarchical control system: 4D/RCS in the DARPA LAGR program

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 11-12 2006
Jim Albus
The Defense Applied Research Projects Agency (DARPA) Learning Applied to Ground Vehicles (LAGR) program aims to develop algorithms for autonomous vehicle navigation that learn how to operate in complex terrain. Over many years, the National Institute of Standards and Technology (NIST) has developed a reference model control system architecture called 4D/RCS that has been applied to many kinds of robot control, including autonomous vehicle control. For the LAGR program, NIST has embedded learning into a 4D/RCS controller to enable the small robot used in the program to learn to navigate through a range of terrain types. The vehicle learns in several ways. These include learning by example, learning by experience, and learning how to optimize traversal. Learning takes place in the sensory processing, world modeling, and behavior generation parts of the control system. The 4D/RCS architecture is explained in the paper, its application to LAGR is described, and the learning algorithms are discussed. Results are shown of the performance of the NIST control system on independently-conducted tests. Further work on the system and its learning capabilities is discussed. © 2007 Wiley Periodicals, Inc. [source]

Enhancing Neural Network Traffic Incident-Detection Algorithms Using Wavelets

COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2001
A. Samant
Researchers have presented freeway traffic incident-detection algorithms by combining the adaptive learning capability of neural networks with imprecision modeling capability of fuzzy logic. In this article it is shown that the performance of a fuzzy neural network algorithm can be improved through preprocessing of data using a wavelet-based feature-extraction model. In particular, the discrete wavelet transform (DWT) denoising and feature-extraction model proposed by Samant and Adeli (2000) is combined with the fuzzy neural network approach presented by Hsiao et al. (1994). It is shown that substantial improvement can be achieved using the data filtered by DWT. Use of the wavelet theory to denoise the traffic data increases the incident-detection rate, reduces the false-alarm rate and the incident-detection time, and improves the convergence of the neural network training algorithm substantially. [source]

A design-variable-based inelastic hysteretic model for beam,column connections

EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS, Issue 4 2008
Gun Jin Yun
Abstract This paper presents a design-variable-based inelastic hysteretic model for beam,column connections. It has been well known that the load-carrying capacity of connections heavily depends on the types and design variables even in the same connection type. Although many hysteretic connection models have been proposed, most of them are dependent on the specific connection type with presumed failure mechanisms. The proposed model can be responsive to variations both in design choices and in loading conditions. The proposed model consists of two modules: physical-principle-based module and neural network (NN)-based module in which information flow from design space to response space is formulated in one complete model. Moreover, owing to robust learning capability of a new NN-based module, the model can also learn complex dynamic evolutions in response space under earthquake loading conditions, such as yielding, post-buckling and tearing, etc. Performance of the proposed model has been demonstrated with synthetic and experimental data of two connection types: extended-end-plate and top- and seat-angle with double-web-angle connection. Furthermore, the design-variable-based model can be customized to any structural component beyond the application to beam,column connections. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Organizational learning and compensation strategies: Evidence from the Spanish chemical industry

HUMAN RESOURCE MANAGEMENT, Issue 3 2005
Pilar Jerez-Gómez
This study explores the role of compensation strategies in promoting organizational learning capability. By analyzing a sample of 111 Spanish firms from the chemical industry, we highlight how compensation strategies can be used to influence employees' commitment to learning and shape their understanding of the overall purpose of the organization. We also show that inappropriate strategies can lead to short-term efficiencies but damage longer-term learning. © 2005 Wiley Periodicals, Inc. [source]

Developing a dynamic project learning and cross-project learning capability: synthesizing two perspectives

INFORMATION SYSTEMS JOURNAL, Issue 6 2008
Sue Newell
Abstract Driven by the complexity of new products and services, project work has become increasingly common in all types of organizations. However, research on project learning suggests that often project teams do not meet their stated objectives and, moreover, there is limited organizational learning from the experiences of project work. We use the dynamic capabilities framework to argue that building a dynamic project learning capability is useful for organizations that make extensive use of projects. We use both survey and interview data to discuss the key ways in which such a dynamic capability can be built. Our survey data demonstrate the importance of documenting project learning, but our interview data show that teams are often remiss at documenting their learning. The results from the two different approaches are synthesized using Boland & Tenkasi's notions of perspective-making and perspective-taking. Importantly, combining the results from the two sets of data suggests that organizations need to emphasize the benefits from project reviews and documentation and explore ways in which the documents produced can be made more useful as boundary objects to encourage the sharing of learning across projects. [source]

Incremental learning of collaborative classifier agents with new class acquisition: An incremental genetic algorithm approach

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2003
Sheng-Uei Guan
A number of soft computing approaches such as neural networks, evolutionary algorithms, and fuzzy logic have been widely used for classifier agents to adaptively evolve solutions on classification problems. However, most work in the literature focuses on the learning ability of the individual classifier agent. This article explores incremental, collaborative learning in a multiagent environment. We use the genetic algorithm (GA) and incremental GA (IGA) as the main techniques to evolve the rule set for classification and apply new class acquisition as a typical example to illustrate the incremental, collaborative learning capability of classifier agents. Benchmark data sets are used to evaluate proposed approaches. The results show that GA and IGA can be used successfully for collaborative learning among classifier agents. © 2003 Wiley Periodicals, Inc. [source]

OUTSOURCING AS SEEN FROM THE PERSPECTIVE OF KNOWLEDGE MANAGEMENT

JOURNAL OF SUPPLY CHAIN MANAGEMENT, Issue 3 2010
OSCAR F. BUSTINZA
This article analyzes outsourcing from a knowledge-based perspective. We investigate how knowledge as an organizational resource and the capabilities to manage this knowledge affect the benefits of outsourcing. Our results indicate that the nature of the knowledge of the outsourced activity affects the success of outsourcing. We also analyze the way in which collaborative know-how (as a knowledge resource) and learning capability (as an organizational capability) affect outsourcing benefits. We then test the validity of this hypothesis by surveying organizations from the service sector. The results of the empirical study provide strong support for our assertion that knowledge management affects the results of outsourcing decisions. [source]

Adaptive neuro-fuzzy models for the quasi-static analysis of microstrip line

MICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 5 2008
Celal Yildiz
Abstract This article presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the effective permittivities and characteristic impedances of microstrip lines. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It has the advantages of expert knowledge of FISs and learning capability of artificial neural networks. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of ANFIS. The results of ANFIS are compared with the results of the experimental works, quasi-static methods, and a commercial electromagnetic simulator IE3D. There is very good agreement among the results of ANFIS models and quasi-static methods, IE3D, and experimental works. © 2008 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 1191,1196, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23322 [source]

Neural Network Adaptive Robust Control Of Siso Nonlinear Systems In A Normal Form

ASIAN JOURNAL OF CONTROL, Issue 2 2001
J.Q. Gong
ABSTRACT In this paper, performance oriented control laws are synthesized for a class of single-input-single-output (SISO) n -th order nonlinear systems in a normal form by integrating the neural networks (NNs) techniques and the adaptive robust control (ARC) design philosophy. All unknown but repeat-able nonlinear functions in the system are approximated by the outputs of NNs to achieve a better model compensation for an improved performance. While all NN weights are tuned on-line, discontinuous projections with fictitious bounds are used in the tuning law to achieve a controlled learning. Robust control terms are then constructed to attenuate model uncertainties for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy. Furthermore, if the unknown nonlinear functions are in the functional ranges of the NNs and the ideal NN weights fall within the fictitious bounds, asymptotic output tracking is achieved to retain the perfect learning capability of NNs. The precision motion control of a linear motor drive system is used as a case study to illustrate the proposed NNARC strategy. [source]