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IEEE International Conference on systems, Man and Cybernetics

作者： S.H. Rubin R.J. Rush M.H. Smith S.N. Jayaram Murthy L. Trajkovic SPAWAR Systems Center San Diego CA USA BISC Program University of California Berkeley CA USA Department of Computer Science Central Michigan University Mount Pleasant MI USA School of Engineering Science Simon Fraser University Burnaby BC Canada

A soft expert system is defined to be one that is qualitatively fuzzy. We present such a system known as KASER which stands for Knowledge Amplification by Structural Expert Randomization. KASER facilitates reasoning using domain specific expert and commonsense knowledge. It accomplishes this through object-classed predicates and an associated novel inference engine. It addresses the high cost associated with the knowledge acquisition bottleneck. It also enables the entry of a basis of rules and provides for the automatic extension of that basis through domain symmetries. We demonstrate an application for KASER in the design of an intelligent tutoring system that teaches the basic science of crystal-laser design. It enables the student to experiment with various design concepts and receive feedback on the functionality of the proposed design. This is possible without a need to preprogram all possible scenarios.

关键词： Expert systems Knowledge acquisition Intelligent systems USA Councils Engines Costs Production systems computer errors Hybrid intelligent systems Fuzzy systems

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From standard to non-standard semantics by semantics modifiers

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International Journal of Foundations of computer science 2001年第2期12卷 171-211页

作者： Abramov, Sergei Glück, Robert Program Systems Institute Russian Academy of Sciences RU-152140 Pereslavl-Zalessky Russian Federation Department of Information and Computer Science School of Science and Engineering Waseda University Shinjuku-ku Tokyo 169-8555 Japan

An approach for systematically modifying the semantics of programming languages by semantics modifiers is described. Semantics modifiers are a class of programs that allow the development of general and reusable semantics components. Language independence is achieved through the interpretive approach: an interpreter serves as a mediator between the new language and the language for which the non-standard semantics was implemented. Inverse computation, equivalence transformation and neighborhood analysis are shown to be semantics modifiers. Experiments with these modifiers show the computational feasibility of this approach. Seven modifier projections are given which allow the efficient implementation of non-standard interpreters and non-standard compilers by program specialization or other powerful program transformation methods. © 2001 World Scientific Publishing Company.

关键词： interpreters non-standard semantics partial evaluation program transformation programming languages supercompilation

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A theoretical model for autism

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Journal of Theoretical Medicine 2001年第4期3卷 271-286页

作者： Vidal De Caralho, Luís Alfred De Carvalho Ferreifa, Níve Fiszman, Adriana Program of Systems Engineering and Computer Science RJ Brazil Institute of Psychiatry Rio de Janeiro Brazil

Autism is a mental disorder characterized by deficits in socialization, communication, and imagination. Along with the deficits, autistic children may show savant skills ("islets of ability") of unknown origin that puzzles their families and the psychologists. Comorbidity with epilepsy and mental retardation has brought the researchers' attention to neurobiological and cognitive theories of the syndrome. The present article proposes a neurobiological model for the autism based on the fundamental biological process of neuronal competition. A neural network capable of defining neural maps-synaptic projections preserving neighborhoods between two neural tissues-simulates the process of neurodevelopment. Experiments were performed reducing the level of neural growth factor released by the neurons, leading to ill-developed maps and suggesting the cause of the aberrant neurogenesis present in autism. The computer simulations hint that brain regions responsible for the formation of higher level representations are impaired in autistic patients. The lack of this integrated representation of the world would result in the peculiar cognitive deficits of socialization, communication, and imagination and could also explain some "islets of abilities", like excellent memory for raw data and stimuli discrimination. The neuronal model is based on plausible biological findings and on recently developed cognitive theories of autism. Close relations are established between the computational properties of the neural network model and the cognitive theory of autism denominated "weak central coherence", bringing some insight to the understanding of the disorder.

关键词： Autism Cortical maps Neural networks Neurodevelopment Plasticity Weak central coherence

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Fuzzy Bayes predictor in electric load forecasting

Fuzzy Bayes predictor in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha V.N.A.L. da Silva G.F. Ribeiro Systems Engineering and Computer Science Program Graduate Programs in Engineering COPPE Federal University of Rio de Janeiro Brazil Electric Power Research Center CEPEL CEPEL Brazil

ISBN: (纸本)0780370449

We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict numerical values. We consider three versions of the FBP, each one with a different dependence among the input data: independence, first-order and second-order dependence. For verifying the efficiency of the FBP's prediction, we compare it with two fuzzy systems and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.

关键词： Load forecasting Fuzzy systems Power engineering and energy Smoothing methods Testing Niobium compounds systems engineering and theory computer science Predictive models Electric variables measurement

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Elastic neural net algorithm for cluster analysis

Elastic neural net algorithm for cluster analysis

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Brazilian Symposium on Neural Networks

作者： R.L. Salvini L.A. V. de Carvalho Program of Systems Engineering and Computer Science COPPE/UFRJ Rio de Janeiro Brazil

Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor in which an elastic ring is attracted by points in a bi-dimensional space while their internal elastic forces try to shun the elastic expansion. The different weights associated with these two kinds of forces lead the elastic to a gradual expansion in the direction of the bi-dimensional points. In this method, the elastic net algorithm is employed with the help of a heuristic framework that improves its performance for application in the n-dimensional space of cluster analysis. Tests were made with two types of data sets: (1) simulated data sets with up to 1000 points randomly generated in groups linearly separable with up to dimension 10 and (2) the Fisher Iris Plant database, a well-known database referred to in the pattern recognition literature. The advantages of the method presented are its simplicity, its fast and stable convergence, beyond efficiency in cluster analysis.

关键词： Neural networks Clustering algorithms Algorithm design and analysis Databases Performance analysis Testing Test pattern generators Iris Pattern recognition Convergence

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Biophysical and spectral characteristics of cool- and warm-season grasslands under three land management practices in Eastern Kansas

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Natural Resources Research 2000年第4期9卷 321-331页

作者： Guo, Xulin Price, Kevin P. Stiles, James M. Kansas Applied Remote Sensing Program (KARS) Department of Geography University of Kansas Lawrence KS 66045 2335 Irving Hill Road United States Radar Systems and Remote Sensing Laboratory (RSL) Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS 66045 2335 Irving Hill Road United States Lawrence KS 66045 2335 Irving Hill Road United States

Grasslands are the largest of the Earth's four major vegetation types and are among the most agriculturally productive lands. Grassland management practices alter biophysical factors, such as plant species composition, soil structure and chemistry, and primary productivity. Large-scale changes in land management practices can have significant impacts on available natural resources, therefore impacting a regional socioeconomics. For these reasons, an improved understanding of the geographic distribution of changing land management practices and how these changes affect grassland biophysical characteristics of an area is critical to improve land use planning and practices. This study investigated the spectral characteristics, as measured using satellite remotely sensed data, of three land management practices [grazing, haying, and management under Conservation Reserve program (CRP)] on areas dominated by cool- and warm-season grasslands. The satellite imagery was acquired in May, July, and September in 1997 over Douglas County, Kansas. Biomass, vegetation cover, and species richness data were collected around the satellite overflight periods. Our results indicate that the biophysical characteristics and spectral reflectances are significantly different among the three management practices over cool- and warm-season grasslands. Interestingly, sites grazed by livestock had the highest species richness and the highest forb cover, although the total cover is almost the same as the other practices, and they have the highest near-infrared reflectance throughout the growing season. © 2000 International Association for Mathematical Geology.

关键词： Landsat Tallgrass prairie Thematic Mapper (TM)

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A comparison of training algorithms for DHP adaptive critic neurocontrol

A comparison of training algorithms for DHP adaptive critic ...

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International Joint Conference on Neural Networks (IJCNN)

作者： G.G. Lendaris T.T. Shannon A. Rustan Systems Science Ph.D. Program and Electrical & Computer Engineering Department Portland State University USA

A variety of alternate training strategies for implementing the dual heuristic programming (DHP) method of approximate dynamic programming in the neurocontrol context are explored. The DHP method of controller training has been successfully demonstrated by a number of authors on a variety of control problems in recent years, but no unified view of the implementation details of the method has yet emerged. A number of options are described for sequencing the training of the controller and critic networks in DHP implementations. Results are given about their relative efficiency and the quality of the resulting controllers for two benchmark control problems.

关键词： Neural networks Shape control Dynamic programming Equations Design optimization Amorphous materials Design methodology Feedback loop Supervised learning Process design

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Recurrent neural gas in electric load forecasting

Recurrent neural gas in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha V.N.A.L. da Silva G.F. Ribeiro Systems Engineering and Computer Science Program-Graduate Programs in Engineering Federal University of Rio de Janeiro Brazil Electric Power Research Center Brazil

We have proposed for the task of hourly electric load forecasting a hybrid neural system combining unsupervised and supervised learning. The system consists of a recurrent neural gas (RNG) network and many Elman neural networks (ENs). RNG is a modification we introduced in the neural gas (NG) network in order to enable it to do clustering using a sequence of input data. For verifying the RNG's performance, many architectures are compared in the learning of global and local models. In a global model only one supervised network is trained and in a local model the training examples are grouped by a clustering algorithm and each one of these groups is sent to different supervised networks. These architectures use different clustering algorithms (NG and RNG) or different supervised networks for prediction (ENs that are trained by backpropagation or backpropagation through time, and feedforward networks).

关键词： Load forecasting Clustering algorithms Neural networks Predictive models Power engineering and energy Backpropagation algorithms Supervised learning systems engineering and theory computer science Recurrent neural networks

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Definition of evaluation criteria for system development acquisition modeling and simulation

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NAVAL ENGINEERS JOURNAL 1999年第1期111卷 55-64页

作者： Leite, MJ Mensh, DR Michael J. Leite:is a Principal Engineer with PRC Inc. a division of Litton Industries. He supports combat system engineering for theater air and missile defense. His other tasks have included the command and control for the AEGIS shipbuilding program systems engineering for the 21st Century Surface Combatant combat system survivability and the development of NATO standardization agreements for naval ordnance. He was previously a Senior Engineer with San Diego Gas & Electric with responsibility for its energy application and lighting programs. Prior to joining SDG&E Mr. Leite was a commissioned officer in the U.S. Navy where he served in operations and engineering assignments. Following active duty he accepted a Naval Reserve commission and has retired with the rank of Captain. His assignments included command operational and engineering tours. Mr. Leite has also served as an expert witness in admiralty and engineering matters. He is a gradate of the University of California Berkeley with a Bachelor of Science Degree in Engineering and also holds a Masters Degree in Business Administration from National University in San Diego. Mr. Leite is a Registered Professional Engineer in the States of California and Minnesota. Mr. Leite is a member of ASNE ASCE MORS the Illuminating Engineering Society and the U.S. Naval institute. Dennis Roy Mensh:is a Senior Engineer with PRC Inc. a division of Litton Industries in Crystal City VA where he supports modeling and simulation tasking for combat systems. He received BS and MS degrees in applied Physics from Lopola College in Baltimore MD and the American University in Washington DC. He has also completed the course work towards a Ph.D. degree in computer science specializing in the fields of Operations Reseurch Anabsis Systems Analysis and Computer Modeling and Simulation. Previously he was employed at the White Oak Laboratory of the Naval Surface Warfare Carter in Silver Spring MD where he worked in the areas of naval sensor and weapon system analysis

This paper defines, develops and examines a set of generic analysis tools that can be applied to Models and Simulations at the systems engineering level of fidelity. The tools examine the performance and effectiveness of Sensors;Weapons;and Battle Management, Command, Control, Communications, computers, and Intelligence ((BMCI)-I-4) systems and equipment. The Measures of Performance (MOPs), Measures of Effectiveness (MOEs) and Measures of Force Effectiveness (MOFEs) were extracted from the Modular Command and Control Structure Paradigm which was developed at the Naval Postgraduate School. The paradigm provides for the development of evaluation criteria (MOPs, MOEs, and MOFEs) in a framework that ensures the traceability of system performance and effectiveness to the system operational requirements as specified in the Operational Requirements Document (ORD). Also, the analysis tools provide insight and valid estimates of numerical measures of the defined system functionality threads, which represent the system's operational requirements as specified in the ORD. The tools are directly transferrable and applicable to test and evaluation exercise events which are conducted in support of the development and acquisition of systems and equipment. Once the levels of system performance have been defined, the Paradigm generates a quantitative database that becomes a useful tool in system tradeoffs and selection. Once the alternative system suites have been defined, the suites can be analyzed in terms of system functionality threads and their corresponding performance capabilities versus cost.

关键词： Naval vessels

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Modelling combat system manning and manpower reduction

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NAVAL ENGINEERS JOURNAL 1999年第3期111卷 153-171页

作者： Roos, CH Carl H. Roos:is a Senior Engineer with Logicon-Syscon. A graduate of the University of Pittsburgh with a BSEE degree he has over 35 years experience in functional operational combat system fire control interface and computer program design. As technology changed and the combat system was upgraded Mr. Roos maintained his level of technical expertise by taking graduate-level courses in computer science modelling & structured analysis networking & fiber optics. He has worked in various capacities on the LHD LHA DDG 993 DD 963 LPD 17 LCC LPD 13 CGN 38 CGN 9 CG 26 and the DDG Class Combat Systems. In recent years Mr. Roos has been responsible for managing directing and performing engineering design and analysis efforts associated with Battle Management Organization (BMO) functional analysis and operational analysis. These efforts were used in defining combat system operational requirements shipboard space requirements and integration requirements. His paper “Configuration Management of Digital Programs” was published at the 1972 IEEE Southeastern Conrence.

NAVSEA 03K41 is responsible for generating Combat System Rattle Management Organizations (BMO) and Functional Flow Diagrams (FFD). Several years ago, NAVSEA provided the resources to conduct a functional analysis that would support the development and validation of the BMOs and FFDs. The major obstacle in performing the analysis was obtaining a consensus on how the functional hierarchy was to be structured. The non-optimum organization of the hierarchy was selected;as a result, the functions were difficult to define, find, use, and validate. Recognizing the shortcomings of this effort, research was conducted to evaluate state-of-the-art structured modelling techniques, concepts, and methodologies. Two modelling concepts by James Martin were found to be applicable for the combat system functional analysis: Enterprise Modelling Concept and Functional Decomposition Modelling Concept. The Structure Modelling definitions of Whitten, Bently, and Barlow provided the guidelines for using the Martin concepts. During the ensuing BMO and FFD development efforts, a Ship's Combat System (SCS) Modelling concept evolved and a SCS Model was developed. This paper addresses how the modelling concepts and tools are used in the BMO and FFD development and validation process. Data from the SCS Model provides the basis for defining combat system requirements (e.g., software, data display, database, networking, etc.).

关键词： Naval vessels

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