Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Heterogeneous tensor decomposition for clustering via manifold optimization

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.

Fusion of motion segmentation algorithms

Many algorithms have been developed to achieve motion segmentation for video surveillance. The algorithms produce varying performances under the infinite amount of changing conditions. It has been recognised that individually these algorithms have useful properties. Fusing the statistical result of these algorithms is investigated, with robust motion segmentation in mind.

Adsorption, orientation and thermal decomposition of copper(II) hexafluoroacetylacetonate on rutile TiO2(110)

We have investigated the adsorption and thermal decomposition of copper hexafluoroacetylacetonate (Cu-11(hfaC)(2)) on single crystal rutile TiO2(110). Low energy electron diffraction shows that room temperature saturation coverage of the Cu-II(hfac)(2) adsorbate forms an ordered (2 x 1) over-layer. X-ray and ultra-violet photoemission spectroscopy of the saturated surface were recorded as the sample was annealed in a sequential manner to reveal decomposition pathways. The results show that the molecule dissociatively adsorbs by detachment of one of the two ligands to form hfac and Cu-1(hfac) which chemisorb to the substrate at 298 K. These ligands only begin to decompose once the surface temperature exceeds 473 K where Cu core level shifts indicate metallisation. This reduction from Cu(I) to Cu(0) takes place in the absence of an external reducing agent and without disproportionation and is accompanied by the onset of decomposition of the hfac ligands. Finally, C K-edge near edge X-ray absorption fine structure experiments indicate that both the ligands adsorb aligned in the < 001 > direction and we propose a model in which the hfac ligands adsorb on the 5-fold coordinated Ti atoms and the Cu-1(hfac) moiety attaches to the bridging O atoms in a square planar geometry. The calculated tilt angle for these combined geometries is approximately 10 degrees to the surface normal.

Decomposition in soil and chemical characteristics of pollen

The input to soils made by pollen and its subsequent mineralization has rarely been investigated from a soil microbiological point of view even though the small but significant quantities of C and N in pollen may make an important contribution to nutrient cycling. The relative resistance to decomposition of pollen exines (outer layers) has led to much of the focus of pollen in soil being on its preservation for archaeological and palaeo-ecological purposes. We have examined aspects of the chemical composition and decomposition of pollen from birch (Betula alba) and maize (Zea mays) in soil. The relatively large N contents, small C-to-N ratios and large water-soluble contents of pollen from both species indicated that they would be readily mineralized in soil. When added to soil and incubated at 16 degrees C an amount of C equivalent to 22-26% of the added pollen C was lost as CO2 within 22 days, with the Z. mays pollen decomposing faster. For B. alba pollen, the water-soluble fraction decomposed faster than the whole pollen and the insoluble fraction decomposed more slowly over 22 days. By contrast, there were no significant differences in the decomposition rates of the different fractions from Z. mays pollen. Solid-state C-13 nuclear magnetic resonance (NMR) revealed no gross chemical differences between the pollen of these two species, with strong resonances in the alkyl- and methyl-C region (0-45 p.p.m.) indicative of aliphatic compounds, the O-alkyl-C (60-90 p.p.m.) and the acetal- and ketal-C region (90-110 p.p.m.) indicative of polysaccharides, and the carbonyl-C region indicative of peptides and carboxylic acids. In addition, both pollens gave a small but distinct resonance at 55 p.p.m. attributed to N-alkyl-C. The resonances attributed to polysaccharides were lost completely or substantially reduced after decomposition.

Decomposition and phosphorus release from organic residues as affected by residue quality and added inorganic phosphorus

The combined use of organic residue and inorganic fertiliser-phosphorus (P) is appropriate in meeting both the short and long-term P requirement of crops. To assess the influence of added inorganic fertiliser-P on the processes of decomposition and P release from the residue and the relationships with quality, prunings of Gliricidia sepium, Leucaena leucocephela, Senna siamea, Acacia mangium and Paraserienthus falcataria were incubated without and with added inorganic fertiliser-P for 56 days. Soil was added only as inoculum. Decomposition rate and amounts of acid extractable-P (P release) were in the same order: G. sepium > S. siamea > L. leucocepheta > P falcataria > A. mangium. Unlike the other residues, A. mangium released no P despite the loss of half its mass during the 8 weeks of incubation. The residue P content correlated with P release. However, decomposition rate did not correlate with residue P content but with the lignin, polyphenol and cellulose content, and ratios to P. These ratios were negatively correlated with P release suggesting that lignin and polyphenol contents influence P release more when the residue-P content is low. Results suggest that rate of decomposition influences the release of P. The critical residue P content for P release was estimated to be 0.12% < P < 0.19%. Added P had no effect on decomposition and P release from the residues.

Decomposition in soil and chemical characteristics of pollen

The input to soils made by pollen and its subsequent mineralization has rarely been investigated from a soil microbiological point of view even though the small but significant quantities of C and N in pollen may make an important contribution to nutrient cycling. The relative resistance to decomposition of pollen exines (outer layers) has led to much of the focus of pollen in soil being on its preservation for archaeological and palaeo-ecological purposes. We have examined aspects of the chemical composition and decomposition of pollen from birch (Betula alba) and maize (Zea mays) in soil. The relatively large N contents, small C-to-N ratios and large water-soluble contents of pollen from both species indicated that they would be readily mineralized in soil. When added to soil and incubated at 16 degrees C an amount of C equivalent to 22-26% of the added pollen C was lost as CO2 within 22 days, with the Z. mays pollen decomposing faster. For B. alba pollen, the water-soluble fraction decomposed faster than the whole pollen and the insoluble fraction decomposed more slowly over 22 days. By contrast, there were no significant differences in the decomposition rates of the different fractions from Z. mays pollen. Solid-state C-13 nuclear magnetic resonance (NMR) revealed no gross chemical differences between the pollen of these two species, with strong resonances in the alkyl- and methyl-C region (0-45 p.p.m.) indicative of aliphatic compounds, the O-alkyl-C (60-90 p.p.m.) and the acetal- and ketal-C region (90-110 p.p.m.) indicative of polysaccharides, and the carbonyl-C region indicative of peptides and carboxylic acids. In addition, both pollens gave a small but distinct resonance at 55 p.p.m. attributed to N-alkyl-C. The resonances attributed to polysaccharides were lost completely or substantially reduced after decomposition.

Spin spaces and positive decomposition of linear maps on ordered Banach spaces

Spin factors and generalizations are used to revisit positive generation of B(E, F), where E and F are ordered Banach spaces. Interior points of B(E, F)+ are discussed and in many cases it is seen that positive generation of B(E, F) is controlled by spin structure in F when F is a JBW-algebra.

Generic systolic array for genetic algorithms

The authors present a systolic design for a simple GA mechanism which provides high throughput and unidirectional pipelining by exploiting the inherent parallelism in the genetic operators. The design computes in O(N+G) time steps using O(N2) cells where N is the population size and G is the chromosome length. The area of the device is independent of the chromosome length and so can be easily scaled by replicating the arrays or by employing fine-grain migration. The array is generic in the sense that it does not rely on the fitness function and can be used as an accelerator for any GA application using uniform crossover between pairs of chromosomes. The design can also be used in hybrid systems as an add-on to complement existing designs and methods for fitness function acceleration and island-style population management

Parallel genetic algorithms for the tuning of a fuzzy AQM controller

This paper presents the results of the application of a parallel Genetic Algorithm (GA) in order to design a Fuzzy Proportional Integral (FPI) controller for active queue management on Internet routers. The Active Queue Management (AQM) policies are those policies of router queue management that allow the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. Two different parallel implementations of the genetic algorithm are adopted to determine an optimal configuration of the FPI controller parameters. Finally, the results of several experiments carried out on a forty nodes cluster of workstations are presented.

Implementing a generic systolic array for genetic algorithms

We have designed a highly parallel design for a simple genetic algorithm using a pipeline of systolic arrays. The systolic design provides high throughput and unidirectional pipelining by exploiting the implicit parallelism in the genetic operators. The design is significant because, unlike other hardware genetic algorithms, it is independent of both the fitness function and the particular chromosome length used in a problem. We have designed and simulated a version of the mutation array using Xilinix FPGA tools to investigate the feasibility of hardware implementation. A simple 5-chromosome mutation array occupies 195 CLBs and is capable of performing more than one million mutations per second. I. Introduction Genetic algorithms (GAs) are established search and optimization techniques which have been applied to a range of engineering and applied problems with considerable success [1]. They operate by maintaining a population of trial solutions encoded, using a suitable encoding scheme.

Systolic random number generation for genetic algorithms

A parallel hardware random number generator for use with a VLSI genetic algorithm processing device is proposed. The design uses an systolic array of mixed congruential random number generators. The generators are constantly reseeded with the outputs of the proceeding generators to avoid significant biasing of the randomness of the array which would result in longer times for the algorithm to converge to a solution. 1 Introduction In recent years there has been a growing interest in developing hardware genetic algorithm devices [1, 2, 3]. A genetic algorithm (GA) is a stochastic search and optimization technique which attempts to capture the power of natural selection by evolving a population of candidate solutions by a process of selection and reproduction [4]. In keeping with the evolutionary analogy, the solutions are called chromosomes with each chromosome containing a number of genes. Chromosomes are commonly simple binary strings, the bits being the genes.