Grey-box radial basis function modelling: the art of incorporating prior knowledge

A basic 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: the underlying data generating mechanism exhibits known symmetric property and the underlying process obeys a set of given boundary value constraints. The class of orthogonal least squares regression algorithms can readily be applied to construct parsimonious grey-box RBF models with enhanced generalisation capability.

A neurofuzzy network knowledge extraction and extended gram-Schmidt algorithm for model subspace decomposition

This paper introduces a new neurofuzzy model construction and parameter estimation algorithm from observed finite data sets, based on a Takagi and Sugeno (T-S) inference mechanism and a new extended Gram-Schmidt orthogonal decomposition algorithm, for the modeling of a priori unknown dynamical systems in the form of a set of fuzzy rules. The first contribution of the paper is the introduction of a one to one mapping between a fuzzy rule-base and a model matrix feature subspace using the T-S inference mechanism. This link enables the numerical properties associated with a rule-based matrix subspace, the relationships amongst these matrix subspaces, and the correlation between the output vector and a rule-base matrix subspace, to be investigated and extracted as rule-based knowledge to enhance model transparency. The matrix subspace spanned by a fuzzy rule is initially derived as the input regression matrix multiplied by a weighting matrix that consists of the corresponding fuzzy membership functions over the training data set. Model transparency is explored by the derivation of an equivalence between an A-optimality experimental design criterion of the weighting matrix and the average model output sensitivity to the fuzzy rule, so that rule-bases can be effectively measured by their identifiability via the A-optimality experimental design criterion. The A-optimality experimental design criterion of the weighting matrices of fuzzy rules is used to construct an initial model rule-base. An extended Gram-Schmidt algorithm is then developed to estimate the parameter vector for each rule. This new algorithm decomposes the model rule-bases via an orthogonal subspace decomposition approach, so as to enhance model transparency with the capability of interpreting the derived rule-base energy level. This new approach is computationally simpler than the conventional Gram-Schmidt algorithm for resolving high dimensional regression problems, whereby it is computationally desirable to decompose complex models into a few submodels rather than a single model with large number of input variables and the associated curse of dimensionality problem. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Knowledge transfer processes in PFI: identification of barriers and enablers

Increasingly, the UK’s Private Finance Initiative has created a demand for construction companies to transfer knowledge from one organization or project to another. Knowledge transfer processes in such contexts face many challenges, due to the many resulting discontinuities in the involvement of organisations, personnel and information flow. This paper empirically identifies the barriers and enablers that hinder or enhance the transfer of knowledge in PFI contexts, drawing upon a questionnaire survey of construction firms. The main findings show that knowledge transfer processes in PFIs are hindered by time constraints, lack of trust, and policies, procedures, rules and regulations attached to the projects. Nevertheless, the processes of knowledge transfer are enhanced by emphasising the value and importance of a supportive leadership, participation/commitment from the relevant parties, and good communication between the relevant parties. The findings have considerable relevance to understanding the mechanism of knowledge transfer between organizations, projects and individuals within the PFI contexts in overcoming the barriers and enhancing the enablers. Furthermore, practitioners and managers can use the findings to efficiently design knowledge transfer frameworks that can be used to overcome the barriers encountered while enhancing the enablers to improve knowledge transfer processes.

Building dynamic capabilities of adaptation and innovation: a study of micro-foundations in a transition economy

What are the microfoundations of dynamic capabilities that sustain competitive advantage in a highly volatile environment, such as a transition economy? We explore the detailed nature of these dynamic capabilities along with their antecedents by tracing the sequence of their development based on a longitudinal case study of an organization subject to an external context of radical transition — the Russian oil company, Yukos. Our rich qualitative data indicate two distinct types of dynamic capabilities that are pivotal for organizational transformation. Adaptation dynamic capabilities relate to routines of resource exploitation and deployment, which are supported by acquisition, internalization and dissemination of extant knowledge, as well as resource reconfiguration, divestment and integration. Innovation dynamic capabilities relate to the creation of completely new capabilities via exploration and path-creation processes, which are supported by search, experimentation and risk taking, as well as project selection, funding and implementation. Second, we find that sequencing the two types of dynamic capabilities, helped the organization both to secure short-term competitive advantage, and to create the basis for long-term competitive advantage. These dynamic capability constructs advance theoretical understanding of what dynamic capabilities are, whilst their sequencing explains how firms create, leverage and enhance them over time.

English language teaching and educational reform in Western China: a knowledge management perspective

This study examines the impact of a large-scale UK-based teacher development programme on innovation and change in English language education in Western China within a knowledge management (KM) framework. Questionnaire data were collected from 229 returnee teachers in 15 cohorts. Follow-up interviews and focus groups were conducted with former participants, middle and senior managers, and teachers who had not participated in the UK programme. The results showed evidence of knowledge creation and amplification at individual, group and inter-organizational levels. However, the present study also identified knowledge creation potential through the more effective organization of follow-up at the national level, particularly for the returnee teachers. It is argued that the KM framework might offer a promising alternative to existing models and metaphors of Continuing Professional Development (CPD).