A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Stereochemical and mechanistic aspects of dioxygenase-catalysed benzylic hydroxylation of indene and chromane substrates

Toluene dioxygenase (TDO)-catalysed benzylic hydroxylation of indene substrates (8, 16 and 17), using whole cell cultures of Pseudomonas putida UV4, was found to yield inden-1-ol (14 and 22) and indan-1-one bioproducts (15 and 23). The formation of these bioproducts is consistent with the involvement of carbon-centred radical intermediates. TDO-catalysed oxidation of indenes 8 and 16 also gave cis-diols 13 and 18 respectively. TDO and naphthalene dioxygenase (NDO), used as both whole-cell preparations and as purified enzymes, were found to catalyse the benzylic hydroxylation of chromane 30, deuteriated (+/-)-chromane 30(D) and enantiomers (4S)-30(D) and (4R)-30(D) to yield (4R)- and (4S)-chroman-4-ols 31/31(D) respectively. The mechanism of benzylic hydroxylation of chromane 30/30(D) involves the stereoselective abstraction of a pro-R (with TDO) or a pro-S (with NDO) hydrogen atom at C-4 and a marked preference for retention of configuration.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.

Antecedents and outcomes of downstream environmental logistics practices

This paper considers the antecedents and outcomes of downstream environmental logistics practices within green supply chain management amongst a sample of respondents based in the UK food industry. Framed through the conceptual lens of the natural resource-based view (NRBV) this research specifically considers (i) whether environmentally proactive companies implement environmental practices downstream in their supply chains as an extension of internal environmental practices and (ii) whether such downstream environmental practices influence performance, particularly when there has been engagement with key stakeholders in their implementation. The paper begins by developing a theoretical model grounded in the NRBV. This model and associated hypotheses are tested using Multivariate Ordinary Least Square (OLS) regression analysis using data from a sample of 149 firms within the UK food industry. The results provide support for a number of the assumptions implicit in the NRBV confirming the link between environmental proactivity and downstream environmental logistics and the important role of internal environmental practices in facilitating this link. The findings also support a direct link between downstream environmental logistics and both environmental and cost performance, which may be enhanced in the presence of high levels of environmental engagement with customers.