Application of parallel distributed processing to space based systems

The concept of using Parallel Distributed Processing (PDP) to enhance automated experiment monitoring and control is explored. Recent very large scale integration (VLSI) advances have made such applications an achievable goal. The PDP machine has demonstrated the ability to automatically organize stored information, handle unfamiliar and contradictory input data and perform the actions necessary. The PDP machine has demonstrated that it can perform inference and knowledge operations with greater speed and flexibility and at lower cost than traditional architectures. In applications where the rule set governing an expert system's decisions is difficult to formulate, PDP can be used to extract rules by associating the information an expert receives with the actions taken

An overview of parallel distributed processing

Parallel Distributed Processing (PDP), or Connectionism, is a frontier cognitive theory that is currently garnering considerable attention from a variety of fields. Briefly summarized herein are the theoretical foundations of the theory, the key elements observed in creating simulation computer programs, examples of its applications, and some comparisons with other models of cognition. A majority of the information is culled from Rumelhart and McClelland\u27s (1986) two volume introduction to the theory, while some concerns from the field and the theorists\u27 accompanying responses are taken from a 1990 article by Hanson and Burr

Parallel distributed processing

After briefly reviewing the appealing psychological properties of PDP systems, an introduction to their historical roots and basic computational mechanisms are provided. A variety of network architectures are described including one-layered perceptrons, backpropagation networks, Boltzmann machines and recurrent systems. Three PDP simulations are analysed: First, a model that purports to learn the past tense of English verbs; Second, a constraint satisfaction network which is able to interpret the alternative configurations of a Necker cube; Finally, a recurrent network which is able to decipher membership of grammatical classes from word-order information. The notion that PDP approaches provide a sub-symbolic account of cognitive processes, in contrast to theclassical symbolic view, is examined. The article concludes with brief speculation concerning the explanatory power of PDP systems at the cognitive level of functioning.After briefly reviewing the appealing psychological properties of PDP systems, an introduction to their historical roots and basic computational mechanisms are provided. A variety of network architectures are described including one-layered perceptrons, backpropagation networks, Boltzmann machines and recurrent systems. Three PDP simulations are analysed: First, a model that purports to learn the past tense of English verbs; Second, a constraint satisfaction network which is able to interpret the alternative configurations of a Necker cube; Finally, a recurrent network which is able to decipher membership of grammatical classes from word-order information. The notion that PDP approaches provide a sub-symbolic account of cognitive processes, in contrast to theclassical symbolic view, is examined. The article concludes with brief speculation concerning the explanatory power of PDP systems at the cognitive level of functioning

Persons Versus Brains: Biological Intelligence in Human Organisms

I go deep into the biology of the human organism to argue that the psychological features and functions of persons are realized by cellular and molecular parallel distributed processing networks dispersed throughout the whole body. Persons supervene on the computational processes of nervous, endocrine, immune, and genetic networks. Persons do not go with brains

Heterogeneous parallel-distributed processing applied to process engineering

The main goal of this thesis was to design new parallel processing strategies specially conceived for distributed environments in order to solve numerical and structural problems from the field of process systems engineering more efficiently. More specifically, the numerical sample problem addressed in this work was the optimization of nonlinear objective functions subjected to sets of nonlinear constraints, while the structural sample problem was the development of parallel-distributed structural techniques for process instrumentation designResumen de la tesis presentada por el autor en el 2002 para la obtención del título de Doctor en Ciencias de la Computación por la Universidad Nacional del Sur.Facultad de Informátic

Neural network based speech synthesizer: A preliminary report

A neural net based speech synthesis project is discussed. The novelty is that the reproduced speech was extracted from actual voice recordings. In essence, the neural network learns the timing, pitch fluctuations, connectivity between individual sounds, and speaking habits unique to that individual person. The parallel distributed processing network used for this project is the generalized backward propagation network which has been modified to also learn sequences of actions or states given in a particular plan

CLASSIFICATION OF COMPLEX TWO-DIMENSIONAL IMAGES IN A PARALLEL DISTRIBUTED PROCESSING ARCHITECTURE

Neural network analysis is proposed and evaluated as a method of analysis of marine biological data, specifically images of plankton specimens. The quantification of the various plankton species is of great scientific importance, from modelling global climatic change to predicting the economic effects of toxic red tides. A preliminary evaluation of the neural network technique is made by the development of a back-propagation system that successfully learns to distinguish between two co-occurring morphologically similar species from the North Atlantic Ocean, namely Ceratium arcticum and C. longipes. Various techniques are developed to handle the indeterminately labelled source data, pre-process the images and successfully train the networks. An analysis of the network solutions is made, and some consideration given to how the system might be extended.Plymouth Marine Laborator

A Study of Energy and Locality Effects using Space-filling Curves

The cost of energy is becoming an increasingly important driver for the operating cost of HPC systems, adding yet another facet to the challenge of producing efficient code. In this paper, we investigate the energy implications of trading computation for locality using Hilbert and Morton space-filling curves with dense matrix-matrix multiplication. The advantage of these curves is that they exhibit an inherent tiling effect without requiring specific architecture tuning. By accessing the matrices in the order determined by the space-filling curves, we can trade computation for locality. The index computation overhead of the Morton curve is found to be balanced against its locality and energy efficiency, while the overhead of the Hilbert curve outweighs its improvements on our test system.Comment: Proceedings of the 2014 IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW