Variable memory

Young, D.L. and Poon, C.-S. 1998. Hebbian covariance learning a nexus for respiratory variability, memory, and optimization In R.L. Hughson, D.A. Cunningham, and J. Duffin Eds.), Advances in Modeling and Control of Ventilation, pp. 73-83, New York, Plenum. 

Defines and allocates memory spaces and initializes variable arrays used in the program. 

We also want to point out the difference between simple rate-dependent phenomena and path-dependent effects. Simple rate dependence means that the internal micromechanical state (as possibly represented by some meso-scale variables) depends only on the current deformation and current rate of deformation the material has no memory of the past. In terms of dislocation dynamics and (7.1), a simple rate-dependent constitutive description would be one in which... 

The time response of the CNC control system is also obtained using SIMUFINK as shown in Figure A1.3. Note that the variables t and xq have been sent to workspace, an area of memory that holds and saves variables. The commands who and whos lists the variables in the workspace. The system time response can be obtained by the command... 

When we can avoid storing the pair variables gjj in the memory, we can save the memory space. In the process of calculating the point distribution function fj, we may use gjj but it is not necessary to store gjj each time. 

Equivalence statement—Assigns two or more variable names to the same memory location... 

This memory erasure problem is sometimes called the credit assignment problem [peret92l. Fortunately, there is an easy way out. We merely generalize the binary (on/off) McCulloch-Pitts neuronal values to continuous variables by smoothing out the step-function threshold. 

With the best observing conditions, it is possible for the trained observer to compete with photoelectric colorimeters for detection of small color differences in samples which can be observed simultaneously. However, the human observer cannot ordinarily make accurate color comparisons over a period of time if memory of sample color is involved. This factor and others, such as variability among observers and color blindness, make it important to control or eliminate the subjective factor in color grading. In this respect, objective methods, which make use of instruments such as spectrophotometers or carefully calibrated colorimeters with conditions of observation carefully standardized, provide the most reliable means of obtaining precise color measurements. 

Owing to the constraints, no direct solution exists and we must use iterative methods to obtain the solution. It is possible to use bound constrained version of optimization algorithms such as conjugate gradients or limited memory variable metric methods (Schwartz and Polak, 1997 Thiebaut, 2002) but multiplicative methods have also been derived to enforce non-negativity and deserve particular mention because they are widely used RLA (Richardson, 1972 Lucy, 1974) for Poissonian noise and ISRA (Daube-Witherspoon and Muehllehner, 1986) for Gaussian noise. 

The MARS program access resource file data by means of general functions which allow the program to load the necessary resource file into memory, search for variables, edit variables, and re-save the file to disk. Any number of resource files can be loaded into memory at once. Even quite large resource files are loaded rapidly into memory since the data is packed and no assignments are made at that time. The search and replace functions are also so fast that data access time is never perceptible. 

A typical performance behaviour is shown in Fig. 44.16b. The increase of the NSE for the monitoring set is a phenomenon that is called overtraining. This phenomenon can be compared to fitting a curve with a polynomial of a too high order or with a PCR or PLS model with too many latent variables. It is caused by the fact that after a certain number of iterations, the noise present in the training set is modelled by the network. The network acts then as a memory, able to recall... 

For small wavevectors the test particle density is a nearly conserved variable and will vary slowly in time. The correlation function in the memory term in the above equation involves evolution, where this slow mode is projected... 

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