900 research outputs found
Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory models
The information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns
Implicit Simulations using Messaging Protocols
A novel algorithm for performing parallel, distributed computer simulations
on the Internet using IP control messages is introduced. The algorithm employs
carefully constructed ICMP packets which enable the required computations to be
completed as part of the standard IP communication protocol. After providing a
detailed description of the algorithm, experimental applications in the areas
of stochastic neural networks and deterministic cellular automata are
discussed. As an example of the algorithms potential power, a simulation of a
deterministic cellular automaton involving 10^5 Internet connected devices was
performed.Comment: 14 pages, 3 figure
Hopfield Neural Network deconvolution for weak lensing measurement
Weak gravitational lensing has the potential to place tight constraints on
the equation of the state of dark energy. However, this will only be possible
if shear measurement methods can reach the required level of accuracy. We
present a new method to measure the ellipticity of galaxies used in weak
lensing surveys. The method makes use of direct deconvolution of the data by
the total Point Spread Function (PSF). We adopt a linear algebra formalism that
represents the PSF as a Toeplitz matrix. This allows us to solve the
convolution equation by applying the Hopfield Neural Network iterative scheme.
The ellipticity of galaxies in the deconvolved images are then measured using
second order moments of the autocorrelation function of the images. To our
knowledge, it is the first time full image deconvolution is used to measure
weak lensing shear. We apply our method to the simulated weak lensing data
proposed in the GREAT10 challenge and obtain a quality factor of Q=87. This
result is obtained after applying image denoising to the data, prior to the
deconvolution. The additive and multiplicative biases on the shear power
spectrum are then +0.000009 and +0.0357, respectively.Comment: 10 pages, 11 figures and 2 tables, accepted for publication in A&
Trajectory prediction of moving objects by means of neural networks
Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 1997Includes bibliographical references (leaves: 103-105)Text in English; Abstract: Turkish and Englishviii, 105 leavesEstimating the three-dimensional motion of an object from a sequence of object positions and orientation is of significant importance in variety of applications in control and robotics. For instance, autonomous navigation, manipulation, servo, tracking, planning and surveillance needs prediction of motion parameters. Although "motion estimation" is an old problem (the formulations date back to the beginning of the century), only recently scientists have provided with the tools from nonlinear system estimation theory to solve this problem eural Networks are the ones which have recently been used in many nonlinear dynamic system parameter estimation context. The approximating ability of the neural network is used to identifY the relation between system variables and parameters of a dynamic system. The position, velocity and acceleration of the object are estimated by several neural networks using the II most recent measurements of the object coordinates as input to the system Several neural network topologies with different configurations are introduced and utilized in the solution of the problem. Training schemes for each configuration are given in detail. Simulation results for prediction of motion having different characteristics via different architectures with alternative configurations are presented comparatively
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