Fast Compaction in Hypercubes

A Fuzzy CMAC Neural Network Model Based on Credit Assignment

In order to improve online learning speed and accuracy of CMAC, a fuzzy CMAC neural network model based on credit assignment concept is designed. In the conventional CMAC and fuzzy CMAC learning scheme, the corrected amounts of errors are equally distributed into all addressed hypercubes, regardless of the credibility of those hypercubes values. The proposed improved learning approach is to use...

Compaction Techniques Post - Pass

ion is vital in order to achieve good compaction. With respect to side effects on performance, we made the following observations. First, the compacted programs become 2%-30% faster, with the speedup averaging approximately 11%. The main reason for this speedup is that, with code abstraction limited to infrequently executed code only, the whole-program optimizations results in far less instruct...

Fast and Slow Compaction in Sedimentary Basins

A mathematical model of compaction in sedimentary basins is presented and analyzed. Compaction occurs when accumulating sediments compact under their own weight, expelling pore water in the process. If sedimentation is rapid or the permeability is low, then high pore pressures can result, a phenomenon which is of importance in oil drilling operations. Here we show that one-dimensional compactio...

An exact, cache-localized algorithm for the sub-quadratic convolution of hypercubes

Fast multidimensional convolution can be performed naively in quadratic time and can often be performed more efficiently via the Fourier transform; however, when the dimensionality is large, these algorithms become more challenging. A method is proposed for performing exact hypercube convolution in sub-quadratic time. The method outperforms FFTPACK, called via numpy, and FFTW, called via pyfftw...

Fast Algorithms for One-Dimensional Compaction with Jog Insertion