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A PDP APPROACH TO LOCALIZED FRACTAL DIMENSION COMPUTATION WITH SEGMENTATION BOUNDARIES

SIMULATION 1995年第1期65卷 26-36页

作者： ROGERS, GW SOLKA, JL PRIEBE, CE GEORGE MASON UNIV CTR COMPUTAT STAT FAIRFAX VA 22030 USA JOHNS HOPKINS UNIV DEPT MATH SCI BALTIMORE MD 21218 USA USN CTR OCEAN SYST SAN DIEGO CA 92152 USA

A parallel distributed processing approach to the computation of localized fractal dimension values in imagery is presented. This approach is a further development of the covering method which requires only nearest neighbor communication. A major benefit of our approach is the ability to readily incorporate any boundary information that may be available. Many fractal textures or surfaces are fractal only in distribution. With this in mind, we show that comparison of the fractal dimension distributions via Kullback-Leibler can give an improved texture discrimination capability over comparison of computed fractal dimension. Results are presented for a set of textures.

关键词： parallel distributed processing FRACTAL DIMENSION COVERING METHOD KULLBACK-LEIBLER

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REPRESENTING KNOWLEDGE BY NEURAL NETWORKS FOR QUALITATIVE-ANALYSIS AND REASONING

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 1995年第5期7卷 683-690页

作者： VAI, MK XU, ZM Department of Electrical and Computer Engineering Northeastern University Boston MA USA

A systematic approach has been developed to construct neural networks for qualitative analysis and reasoning. These neural networks are used as specialized parallel distributed processors for solving constraint satisfaction problems. A typical application of such a neural network is to determine a reasonable change of a system after one or more of its variables are changed. A six-node neural network is developed to represent fundamental qualitative relations. A larger neural network can be constructed hierarchically for a system to be modeled by using six-node neural networks as building blocks. The complexity of the neural network building process is thus kept manageable. An example of developing a neural network reasoning model for a transistor equivalent circuit is demonstrated. The use of this neural network model in the equivalent circuit parameter extraction process is also described.

关键词： KNOWLEDGE REPRESENTATION QUALITATIVE REASONING CONSTRAINT SATISFACTION parallel distributed processing NEURAL NETWORKS EXPERT SYSTEMS

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parallel distributed-processing AND NEUROPSYCHOLOGY - A NEURAL NETWORK MODEL OF WISCONSIN CARD SORTING AND VERBAL FLUENCY

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NEUROPSYCHOLOGY REVIEW 1992年第2期3卷 213-233页

作者： PARKS, RW LEVINE, DS LONG, DL CROCKETT, DJ DALTON, IE WEINGARTNER, H FEDIO, P COBURN, KL SILER, G MATTHEWS, JR BECKER, RE NIMH CLIN SCI LABBETHESDAMD 20892 NIMH PSYCHOTHERAPEUT MEDICAT DEV PROGRAMBETHESDAMD 20892 SO ILLINOIS UNIV SCH MEDDEPT PSYCHIATDIV NEUROPSYCHOLSPRINGFIELDIL 62794 DIGITAL SYST RESOURCES FAIRFAXVA UNIV TEXAS DEPT MATHARLINGTONTX 76019 UNIV BRITISH COLUMBIA DEPT PSYCHIATVANCOUVER V6T 1W5BCCANADA NINCDS BETHESDAMD 20892 UNIV CALIF DAVIS DEPT PSYCHOLDAVISCA 95616 MERCER UNIV SCH MEDDEPT PSYCHIATMACONGA 31207

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.

关键词： parallel distributed processing NEURAL NETWORKS ASSOCIATIONISM ARTIFICIAL INTELLIGENCE WISCONSIN CARD SORTING VERBAL FLUENCY NEUROPSYCHOLOGY

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parallel distributed-processing AND NEURAL NETWORKS - ORIGINS, METHODOLOGY AND COGNITIVE FUNCTIONS

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INTERNATIONAL JOURNAL OF NEUROSCIENCE 1991年第3-4期60卷 195-214页

作者： PARKS, RW LONG, DL LEVINE, DS CROCKETT, DJ MCGEER, EG MCGEER, PL DALTON, IE ZEC, RF BECKER, RE COBURN, KL SILER, G NELSON, ME BOWER, JM SO ILLINOIS UNIV SCH MED DEPT PSYCHIAT SPRINGFIELD IL 62708 USA MERCER UNIV SCH MED DEPT PSYCHIAT MACON GA 31207 USA UNIV CALIF DAVIS DEPT PSYCHOL DAVIS CA 95616 USA DIGITAL SYST RESOURCES FAIRFAX VA USA UNIV TEXAS DEPT MATH AUSTIN TX 78712 USA UNIV BRITISH COLUMBIA DEPT PSYCHIAT VANCOUVER V6T 1W5 BC CANADA CALTECH DEPT BIOL PASADENA CA 91125 USA

parallel distributed processing (PDP), a computational methodology with origins in Associationism, is used to provide empirical information regarding neurobiological systems. Recently, supercomputers have enabled neuroscientists to model brain behavior-relationships. An overview of supercomputer architecture demonstrates the advantages of parallel over serial processing. Histological data provide physical evidence of the parallel distributed nature of certain aspects of the human brain, as do corresponding computer simulations. Whereas sensory networks follow more sequential neural network pathways, in vivo brain imaging studies of attention and rudimentary language tasks appear to involve multiple cortical and subcortical areas. Controversy remains as to whether associative models or Artificial Intelligence symbolic models better reflect neural networks of cognitive functions; however, considerable interest has shifted towards associative models.

关键词： parallel distributed processing NEURAL NETWORKS ASSOCIATIONISM POSITRON EMISSION TOMOGRAPHY ARTIFICIAL INTELLIGENCE NEUROPSYCHOLOGY COGNITION

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CONNECTIONISM, COGNITIVE MAPS AND THE DEVELOPMENT OF OBJECTIVITY

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ARTIFICIAL INTELLIGENCE REVIEW 1993年第5期7卷 329-354页

作者： CHRISLEY, RL UNIV SUSSEX SCH COGNIT & COMP SCIBRIGHTON BN1 9RHE SUSSEXENGLAND

It is claimed that there are pre-objective phenomena, which cognitive science should explain by employing the notion of non-conceptual representational content. It is argued that a match between parallel distributed processing (PDP) and non-conceptual content (NCC) not only provides a means of refuting recent criticisms of PDP as a cognitive architecture;it also provides a vehicle for NCC that is required by naturalism. A connectionist cognitive mapping algorithm is used as a case study to examine the affinities between PDP and NCC.

关键词： COGNITIVE ARCHITECTURE COGNITIVE MAP CONCEPT CONCEPT LEARNING CONNECTIONISM CONTENT CONTEXT-SENSITIVITY DEVELOPMENT GENERALITY INTENTIONALITY REPRESENTATION NON-CONCEPTUAL CONTENT parallel distributed processing SUB-SYMBOLIC COMPUTATION SYSTEMATICITY

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Autonomous Grasp Control of Link Mechanism by Vibrating Potential Method

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IFAC Proceedings Volumes 1993年第2期26卷 859-862页

作者： H. Yokoi Y. Kakazu Faculty of Engineering Department of Precision Engineering Hokkaido University Sapporo 060 Japan

This paper aims at developing a new control method based on the decentralized management approache to describing a mathematical model of the autonomic machiens. In the field of engineering, there were formerly two categories of the control method. One was based on a central control, the other was on parallel distribution. However, these former models are based on the connection between processing units. Thus each processing unit must be controled by a host unit. Both of these units have to be programmed by a sequence of procedures, and all the units are hardly able to express unification and enlargement. However, in Nature there is an important method that utilizes the propagation of information, for example light waves, sonic waves and smell; and it is known that information transmission among all life units, not just animal, depends on this method. Furthermore, the infonnation processing of a life unit is a self-organizing process that can be achieved by utilizing a field to exchange information. By this method, there are many cases where high perfonnance information processing can be attained without supervision. IIere, in order to develop a more generalized theory for the control of autonomic machines, the Vibrating Potential Method (VPM) is introduced. The VPM is described by placing each unit's potential functions on its own harmonic wave as axes. Each unit receives potential energy from the Vibrating Potential Field (VPF) by convolution and decides its own strategy of motion independently. This paper proposes a new control method using field and discusses its application to the grasping problem of free form objects.

关键词： Automatic control information theory parallel distributed processing vibration measurement vibrating potential method wave motion

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MULTILAYER parallel distributed PATTERN-RECOGNITION SYSTEM MODEL USING SPARSE RAM NETS

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IEE PROCEEDINGS-E COMPUTERS AND DIGITAL TECHNIQUES 1992年第2期139卷 144-146页

作者： YANG, GQ CHEN, SC LU, J Department of Computer Science and Engineering Nanjing Aeronautical Institute Nanjing China

We propose a novel multilayer parallel distributed pattern recognition system model in which the n-tuple principle in WISARD is followed and ordinary RAM nets are replaced by sparse RAM ones. The new system considers pattern correlation and is able to optimise n-tuple size in a larger range through reduction of cost. Preliminary experiments with handwritten Chinese character recognition have confirmed the feasibility of the model.

关键词： parallel distributed processing PATTERN RECOGNITION SPARSE distributed MEMORY CHINESE CHARACTER RECOGNITION

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Adapted and Adaptive Properties in Neural Networks for Visual Pattern Discrimination: A Neurobiological Analysis Toward Neural Engineering

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Adaptive Behavior 1992年第2期1卷 123-154页

作者： Ewert, J.-P. Buxbaum-Conradi, H. Fingerling, S. Schürg-Pfeiffer, E. Beneke, T.W. Dinges, A.W. Glagow, M. Schwippert, W.W. University of Kassel Germany

A topic of interdisciplinary research in neurobiology and neuroinformatics concerns visual pattern recognition by neuronal networks. Drawing on quantitative studies of visual releasers of prey catching in toads, it can be shown that moving objects are classified based on an evaluation of certain configurational features. The information regarding these features is provided in the manner of parallel distributed processing within a retino-pretectal-tectal interacting network. This processing structure is, to a considerable extent, modifiable and adaptive. Associative and nonassociative learning processes take advantage of loop operations involving various forebrain structures. An artificial neuronal net, applying some principles of the toad's visual system, is tested to promote the dialogue between neurobiology and engineering. © 1992, Sage Publications. All rights reserved.

关键词： gating implicit computation learning modulation parallel distributed processing prey catching sensorimotor codes toad Visual pattern recognition

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Connectionism, processing, Memory

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Connection science 1992年第3-4期4卷 207-226页

作者： GEORG SCHWARZ

computing devices such as Turing machines resolve the dilemma between the necessary finitude of effective procedures and the potential infinity of a function's domain by distinguishing between a finite-state processing part, defined over finitely many representation types, and a memory sufficiently large to contain representation tokens for any of the function's arguments and values. Connectionist networks have been shown to be (at least) Turing-equivalent if provided with infinitely many nodes or infinite-precision activation values and weights. Physical computation, however, is necessarily finite.

关键词： Computation representation connectionism neural networks parallel distributed processing

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MODELING RED PINE TREE SURVIVAL WITH AN ARTIFICIAL NEURAL NETWORK

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FOREST SCIENCE 1991年第5期37卷 1429-1440页

作者： GUAN, BT GERTNER, G Postdoctoral researcher Department of Forestry University of Illinois 110 Mumford Hall 1301 West Gregory Drive Urbana IL 61801 Associate Professor of Forest Biometrics Department of Forestry University of Illinois 110 Mumford Hall 1301 West Gregory Drive Urbana IL 61801

A model based on an Artificial Neural Network (ANN) was developed for modeling and predicting red pine survival. The new model uses diameter at breast height and estimated annual diameter growth as its predictors. For training neural networks, a proportional coding scheme based on Gaussian distributions was used to transform the data into patterns of activities. Four model performance criteria-sum of square errors (SSE), chi-2 statistic, final predicted error (FPE), and predicted squared error (PSE)-were used to determine the adequacy of the new model. Based on the four criteria, the ANN-based new red pine survival model not only fits the data better than a statistical model;it is also expected to perform better on future data, provided that the training data are representative. The response surface of the ANN model shows it has the required flexibility to model red pine survival, especially in modeling both small and large but slow growing trees. This study also shows that a proportionally coded training data set may indeed be an effective form of input data representation for developing red pine survival models based on artificial neural networks.

关键词： MORTALITY MODEL parallel distributed processing BACK PROPAGATION NONLINEAR APPROXIMATION

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