A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Detection of somaclonal variation during cocoa somatic embryogenesis characterised using cleaved amplified polymorphic sequence and the new freeware Artbio

The scarcity and stochastic nature of genetic mutations presents a significant challenge for scientists seeking to characterise de novo mutation frequency at specific loci. Such mutations can be particularly numerous during regeneration of plants from in vitro culture and can undermine the value of germplasm conservation efforts. We used cleaved amplified polymorphic sequence (CAPS) analysis to characterise new mutations amongst a clonal population of cocoa plants regenerated via a somatic embryogenesis protocol used previously for cocoa cryopreservation. Efficacy of the CAPS system for mutation detection was greatly improved after an ‘a priori’ in silico screen of reference target sequences for actual and potential restriction enzyme recognition sites using a new freely available software called Artbio. Artbio surveys known sequences for existing restriction enzyme recognition sites but also identifies all single nucleotide polymorphism (SNP) deviations from such motifs. Using this software, we performed an in silico screen of seven loci for restriction sites and their potential mutant SNP variants that were possible from 21 restriction enzymes. The four most informative locus-enzyme combinations were then used to survey the regenerant populations for de novo mutants. We characterised the pattern of point mutations and, using the outputs of Artbio, calculated the ratio of base substitution in 114 somatic embryo-derived cocoa regenerants originating from two explant genotypes. We found 49 polymorphisms, comprising 26.3% of the samples screened, with an inferred rate of 2.8 × 10−3 substitutions/screened base. This elevated rate is of a similar order of magnitude to previous reports of de novo microsatellite length mutations arising in the crop and suggests caution should be exercised when applying somatic embryogenesis for the conservation of plant germplasm.

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.

Modeling camera orientation and 3D structure from a sequence of images taken by a perambulating commercial video camera

In this paper we report the degree of reliability of image sequences taken by off-the-shelf TV cameras for modeling camera rotation and reconstructing 3D structure using computer vision techniques. This is done in spite of the fact that computer vision systems usually use imaging devices that are specifically designed for the human vision. Our scenario consists of a static scene and a mobile camera moving through the scene. The scene is any long axial building dominated by features along the three principal orientations and with at least one wall containing prominent repetitive planar features such as doors, windows bricks etc. The camera is an ordinary commercial camcorder moving along the axial axis of the scene and is allowed to rotate freely within the range +/- 10 degrees in all directions. This makes it possible that the camera be held by a walking unprofessional cameraman with normal gait, or to be mounted on a mobile robot. The system has been tested successfully on sequence of images of a variety of structured, but fairly cluttered scenes taken by different walking cameramen. The potential application areas of the system include medicine, robotics and photogrammetry.

Phylogenetic systematics of Sternbergia (Amaryllidaceae) based on plastid and ITS sequence data

The phylogenetics of Sternbergia (Amaryllidaceae) were studied using DNA sequences of the plastid ndhF and matK genes and nuclear internal transcribed spacer (ITS) ribosomal region for 38, 37 and 32 ingroup and outgroup accessions, respectively. All members of Sternbergia were represented by at least one accession, except S. minoica and S. schubertii, with additional taxa from Narcissus and Pancratium serving as principal outgroups. Sternbergia was resolved and supported as sister to Narcissus and composed of two primary subclades: S. colchiciflora sister to S. vernalis, S. candida and S. clusiana, with this clade in turn sister to S. lutea and its allies in both Bayesian and bootstrap analyses. A clear relationship between the two vernal flowering members of the genus was recovered, supporting the hypothesis of a single origin of vernal flowering in Sternbergia. However, in the S. lutea complex, the DNA markers examined did not offer sufficient resolving power to separate taxa, providing some support for the idea that S. sicula and S. greuteriana are conspecific with S. lutea