Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence

In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.

Grey-box radial basis function modelling

A fundamental principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: (i) the underlying data generating mechanism exhibits known symmetric property, and (ii) the underlying process obeys a set of given boundary value constraints. The class of efficient orthogonal least squares regression algorithms can readily be applied without any modification to construct parsimonious grey-box RBF models with enhanced generalisation capability.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

Reduced complexity set-point following in self-tuning control

An external input signal is incorporated into a self-tuning controller which, although it is based on a CARMA system model, employs a state-space framework for control law calculations. Steady-state set point following can then be accomplished even when only a recursive least squares parameter estimation scheme is used, despite the fact that the disturbance affecting the system may well be coloured.

Practical neural networks in a computer control package

This paper deals with the integration of radial basis function (RBF) networks into the industrial software control package Connoisseur. The paper shows the improved modelling capabilities offered by RBF networks within the Connoisseur environment compared to linear modelling techniques such as recursive least squares. The paper also goes on to mention the way this improved modelling capability, obtained through the RBF networks will be utilised within Connoisseur.

Implementation of microprocessor based self-tuning control on a heating system

This paper describes the implementation, using a microprocessor, of a self-tuning control algorithm on a heating system. The algorithm is based on recursive least squares parameter estimation with a state-space, pole placement design criterion and shows how the controller behaves when applied to an actual system.

Determination by MAD-DM of the structure of the DNA duplex d[ACGTACG(5-BrU)]2 at 1.46 Å and 100 K

A four-wavelength MAD experiment on a new brominated octanucleotide is reported here. d[ACGTACG(5-BrU)], C77H81BrN30O32P7, (DNA) = 2235, tetragonal, P43212 (No. 96), a = 43.597, c = 26.268 Å, V = 49927.5 Å3, Z = 8, T = 100 K, R = 10.91% for 4312 reflections between 15.0 and 1.46 Å resolution. The self-complementary brominated octanucleotide d[ACGTACG(5-BrU)]2 has been crystallized and data measured to 1.45 Å at both 293 K and a second crystal flash frozen at 100 K. The latter data collection was carried out to the same resolution at the four wavelengths 0.9344, 0.9216, 0.9208 and 0.9003 Å, around the Br K edge at 0.92 Å and the structure determined from a map derived from a MAD data analysis using pseudo-MIR methodology, as implemented in the program MLPHARE. This is one of the first successful MAD phasing experiments carried out at Sincrotrone Elettra in Trieste, Italy. The structure was refined using the data measured at 0.9003 Å, anisotropic temperature factors and the restrained least-squares refinement implemented in the program SHELX96, and the helical parameters are compared with those previously determined for the isomorphous d(ACGTACGT)2 analogue. The asymmetric unit consists of a single strand of octamer with 96 water molecules. No countercations were located. The A-DNA helix geometry obtained has been analysed using the CURVES program.

Spectroscopic properties of alkenylferrocenes; crystal and molecular structure of (Z)-(1,2-diphenylethenyl)ferrocene

Studies of the 1H n.m.r. and electronic spectra of a series of alkenylferrocenes including (E) and (Z) stereoisomers of various styrylferrocenes, have provided methods of structure elucidation. Crystals of the title compound are monoclinic, space group P21/c with Z= 4 in a unit cell of dimensions a= 17.603(2), b= 10.218(2), c= 10.072 Å, β= 103.27(2)°. The structure has been determined by the heavy-atom method from diffractometer data and refind by full-matrix least-squares techniques to R= 0.043 for 2 219 unique reflections.

Unsaturated σ-hydrocarbyl transition-metal complexes. Part 4. Crystal and molecular structure of trans-chlorobis(diethylphenylphosphine)-(phenylethynyl)platinum(II) and comments on the relative trans influence of various carbon ligands

The molecular structure of trans-[PtCl(CCPh)(PEt2Ph)2] has been determined by X-ray diffraction methods. The crystals are monoclinic, space group P21, with a= 12.359(3), b= 13.015(3), c= 9.031(2)Å, β= 101.65(2)°, and Z= 2. The structure has been solved by the heavy-atom method and refined by full-matrix least squares to R 0.046 for 1 877 diffractometric intensity data. The crystals contain discrete molecules in which the platinum coordination is square planar. The phenylethynyl group is non-linear, with a Pt–CC angle of 163(2)°. Selected bond lengths are Pt–Cl 2.407(5) and Pt–C 1.98(2)Å. The structural trans influences of CCPh, CHCH2, and CH2SiMe3 ligands in platinum(II) complexes are compared; there is only a small dependence on hybridization at the ligating carbon atom.

Unsaturated σ-hydrocarbyl transition-metal complexes. Part 3. Crystal and molecular structure of trans-chlorobis(diethylphenylphosphine)(vinyl)platinum(II)

The molecular structure of trans-[PtCl(CHCH2)(PEt2Ph)2] has been determined by X-ray diffraction methods. The crystals are orthorhombic, space group Pbcn, with a= 10.686(2), b= 13.832(4), c= 16.129(4)Å, and Z= 4. The structure has been solved by the heavy-atom method and refined by full-matrix least squares to R 0.044 for 1 420 diffractometric intensity data. The crystals contain discrete molecules in which the platinum co-ordination is square planar. The Pt–Cl bond vector coincides with a crystallographic diad axis about which the atoms of the vinyl group are disordered. Selected bond lengths (Å) are Pt–Cl 2.398(4), Pt–P 2.295(3), and Pt–C 2.03(2). The Pt–CC angle is 127(2)°. From a survey of the available structural data it is concluded that there is little, if any, back donation from platinum to carbon in platinum–alkenyl linkages.

Molybdate and tungstate transfer by rat ileum competitive inhibition by sulphate

For both MoO42− and WO42− the maximum rate of uptake by the small intestine of the rat (studied in vitro using the everted sac technique) occurs in the lower ileum. Kinetic constants, derived by a least squares procedure, are compared with those previously obtained for SO42− transport. For both and , , with only small differences between sacs IV and V. Mutual inhibition of MoO42− and WO42− transport and inhibition of both by SO42− are competitive processes. This is shown by the generally good agreement between values and derived values and by V values in the presence and absence of the inhibiting species. The three ions SO42−, MoO42− and WO42− are probably transferred across the intestine by a common carrier system. Implications for the sulphate-molybdenum interaction in molybdosis are discussed.

Prediction of coagulation properties, titratable acidity and pH of bovine milk using mid-infrared spectroscopy

This study investigated the potential application of mid-infrared spectroscopy (MIR 4,000–900 cm−1) for the determination of milk coagulation properties (MCP), titratable acidity (TA), and pH in Brown Swiss milk samples (n = 1,064). Because MCP directly influence the efficiency of the cheese-making process, there is strong industrial interest in developing a rapid method for their assessment. Currently, the determination of MCP involves time-consuming laboratory-based measurements, and it is not feasible to carry out these measurements on the large numbers of milk samples associated with milk recording programs. Mid-infrared spectroscopy is an objective and nondestructive technique providing rapid real-time analysis of food compositional and quality parameters. Analysis of milk rennet coagulation time (RCT, min), curd firmness (a30, mm), TA (SH°/50 mL; SH° = Soxhlet-Henkel degree), and pH was carried out, and MIR data were recorded over the spectral range of 4,000 to 900 cm−1. Models were developed by partial least squares regression using untreated and pretreated spectra. The MCP, TA, and pH prediction models were improved by using the combined spectral ranges of 1,600 to 900 cm−1, 3,040 to 1,700 cm−1, and 4,000 to 3,470 cm−1. The root mean square errors of cross-validation for the developed models were 2.36 min (RCT, range 24.9 min), 6.86 mm (a30, range 58 mm), 0.25 SH°/50 mL (TA, range 3.58 SH°/50 mL), and 0.07 (pH, range 1.15). The most successfully predicted attributes were TA, RCT, and pH. The model for the prediction of TA provided approximate prediction (R2 = 0.66), whereas the predictive models developed for RCT and pH could discriminate between high and low values (R2 = 0.59 to 0.62). It was concluded that, although the models require further development to improve their accuracy before their application in industry, MIR spectroscopy has potential application for the assessment of RCT, TA, and pH during routine milk analysis in the dairy industry. The implementation of such models could be a means of improving MCP through phenotypic-based selection programs and to amend milk payment systems to incorporate MCP into their payment criteria.

Visible-near infrared spectroscopy sensor for predicting curd and whey composition during cheese processing

The potential of visible-near infrared spectra, obtained using a light backscatter sensor, in conjunction with chemometrics, to predict curd moisture and whey fat content in a cheese vat was examined. A three-factor (renneting temperature, calcium chloride, cutting time), central composite design was carried out in triplicate. Spectra (300–1,100 nm) of the product in the cheese vat were captured during syneresis using a prototype light backscatter sensor. Stirring followed upon cutting the gel, and samples of curd and whey were removed at 10 min intervals and analyzed for curd moisture and whey fat content. Spectral data were used to develop models for predicting curd moisture and whey fat contents using partial least squares regression. Subjecting the spectral data set to Jack-knifing improved the accuracy of the models. The whey fat models (R = 0.91, 0.95) and curd moisture model (R = 0.86, 0.89) provided good and approximate predictions, respectively. Visible-near infrared spectroscopy was found to have potential for the prediction of important syneresis indices in stirred cheese vats.

Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy

The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict Miscanthus xgiganteus and short rotation coppice willow quality indices was examined. Moisture, calorific value, ash and carbon content were predicted with a root mean square error of cross validation of 0.90% (R2 = 0.99), 0.13 MJ/kg (R2 = 0.99), 0.42% (R2 = 0.58), and 0.57% (R2 = 0.88), respectively. The moisture and calorific value prediction models had excellent accuracy while the carbon and ash models were fair and poor, respectively. The results indicate that near infrared spectroscopy has the potential to predict quality indices of dedicated energy crops, however the models must be further validated on a wider range of samples prior to implementation. The utilization of such models would assist in the optimal use of the feedstock based on its biomass properties.

Evaluating mid-infrared spectroscopy as a new technique for predicting sensory texture attributes of processed cheese

The objective of this study was to investigate the potential application of mid-infrared spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates mid-infrared spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n = 32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4 degrees C, mid-infrared spectra ( 640 to 4,000 cm(-1)) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range ( 930 to 1,767 cm(-1)). The remaining attributes were most successfully modeled using a combined range ( 930 to 1,767 cm(-1) and 2,839 to 4,000 cm(-1)). The root mean square errors of cross-validation for the models were 7.4(firmness; range 65.3), 4.6 ( rubbery; range 41.7), 7.1 ( creamy; range 60.9), 5.1(chewy; range 43.3), 5.2(mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 ( melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions ( range error ratio = 9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that mid-infrared spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality..