Variable selection procedures and efficient suboptimal mask search algorithms in fuzzy inductive reasoning

This paper describes two new suboptimal mask search algorithms for Fuzzy inductive reasoning (FIR), a technique for modelling dynamic systems from observations of their input/output behaviour. Inductive modelling is by its very nature an optimisation problem. Modelling large-scale systems in this fashion involves solving a high-dimensional optimisation problem, a task that invariably carries a high computational cost. Suboptimal search algorithms are therefore important. One of the two proposed algorithms is a new variant of a directed hill-climbing method. The other algorithm is a statistical technique based on spectral coherence functions. The utility of the two techniques is demonstrated by means of an industrial example. A garbage incinerator process is inductively modelled from observations of 20 variable trajectories. Both suboptimal search algorithms lead to similarly good models. Each of the algorithms carries a computational cost that is in the order of a few percent of the cost of solving the complete optimisation problem. Both algorithms can also be used to filter out variables of lesser importance, i.e. they can be used as variable selection tools.     

On qualitative modelling

Fundamental assumptions behind qualitative modelling are critically considered and some inherent problems in that modelling approach are outlined. The problems outlined are due to the assumption that a sufficient set of symbols representing the fundamental features of the physical world exists. That assumption causes serious problems when modelling continuous systems. An alternative for intelligent system building for cases not suitable for qualitative modelling is proposed. The proposed alternative combines neural networks and quantitative modelling.     

Qualitative modelling for automatic identification of mathematical models of chemical reaction systems

Mathematical modelling and identification of systems, where the structure of some equations is unknown, is a difficult task. As this is usually done manually by a human expert, it is associated with terms such as experience and intuition. However, an experienced modeller will have a clear procedure to deduce a model. In a first step he or she will analyse experimental data on a qualitative level. Only those models are then tested in a quantitative identification which pass the qualitative check. In this contribution this efficient procedure is translated into methods. A tool TAM-C is introduced which automatically finds structures and parameters of formal kinetics. The main part of the contribution is dedicated to the qualitative preselection of possible model candidates and the application to an industrial process.     

On observer-based fault detection for nonlinear systems

This paper addresses analysis and integrated design of observer-based fault detection (FD) for nonlinear systems. To gain a deeper insight into the observer-based FD framework, definitions and existence conditions for nonlinear observer-based FD systems are studied first. Then, a scheme for an integrated design of observer-based FD systems for affine nonlinear systems is proposed. Our work is considerably inspired by the study on input–output stability and stabilization of nonlinear systems. Examples are given at the end of the paper to illustrate the theoretical results.     

A fuzzy analysis of country-size argument for the Feldstein-Horioka puzzle

This study aims to analyze the effect of country size, represented by relative Gross National Products (GNP), on the association between domestic investment and saving, using data from a panel of 21 OECD countries. The countries are clustered into four groups with respect to their relative country sizes with an application of Fuzzy c-Means clustering technique. The novelty of this approach is that it is an unsupervised method that generates membership values between zero and one instead of binary values that take values of zero or one only. In addition to this it has the advantages of its tolerance to imprecise data and the ease of understanding. The results show that the saving retention coefficients are greater for larger countries except for the cluster which contains the largest country. Thus, this work presents only partial evidence that the country size affects the relationship of domestic saving and investment.     

On fuzzifications of discrete dynamical systems

Let X denote a locally compact metric space and φ : X → X be a continuous map. In the 1970s Zadeh presented an extension principle helping us to fuzzify the dynamical system (X, φ), i.e., to obtain a map Φ for the space of fuzzy sets on X. We extend an idea mentioned in [P. Diamond, A. Pokrovskii, Chaos, entropy and a generalized extension principle, Fuzzy Sets Syst. 61 (1994) 277-283] to generalize Zadeh’s original extension principle.In this paper we study basic properties of so-called g-fuzzifications, such as their continuity properties. We also show that, for any g-fuzzification: (i) a uniformly convergent sequence of uniformly continuous maps on X induces a uniformly convergent sequence of fuzzifications on the space of fuzzy sets and (ii) a conjugacy (resp., a semi-conjugacy) between two discrete dynamical systems can be extended to a conjugacy (resp., a semi-conjugacy) between fuzzified dynamical systems.Throughout this paper we consider different topological structures in the space of fuzzy sets, namely, the sendograph, endograph and levelwise topologies.     

On the selection of control configurations for uncertain systems using gramian-based Interaction Measures

A critical step in the control design of industrial processes is the Control Configuration Selection (CCS), where each actuator is associated with a set of measurements which will be used in the calculation of the control action.A possible solution to the CCS problem is given by the gramian-based Interaction Measures (IMs), which are derived from nominal process models. This paper introduces the derivation of uncertainty bounds for a gramian-based IM using models with uncertainty described in multiplicative form. An alternative to this model-based approach is presented, where uncertainty bounds are estimated from a tailored experiment.In addition, a procedure for robust CCS is introduced. This procedure integrates the calculated uncertainty bounds in the design of the control configuration.     

Query-based verification of qualitative trends and oscillations in biochemical systems

We investigate the application of query-based verification to the analysis of behavioural trends of stochastic models of biochemical systems. We derive temporal logic properties which address specific behavioural questions, such as the likelihood for a species to reach a peak/deadlock state, or to exhibit monotonic/oscillatory trends. We introduce a specific modelling convention through which stochastic models of biochemical systems are made suitable to verification of the behavioural queries we define. Based on the queries we identify, we define a classification procedure which, given a stochastic model, allows for identifying meaningful qualitative behavioural trends. We illustrate the proposed query-based classification on a number of simple abstract models of biochemical systems.     

Risk assessment modelling of microbiology-related solids separation problems in activated sludge systems

This paper proposes a risk assessment model for settling problems of microbiological origin in activated sludge systems (filamentous bulking, foaming and rising sludge). The aim of the model is not to diagnose microbiology-related solids separation problems with absolute certainty but to quantify in dynamic scenarios whether simulated operational procedures and control strategies lead to favourable conditions for them to arise or not. The rationale behind the model (which integrates the mechanisms of standard activated sludge models with empirical knowledge), its implementation in a fuzzy rule-based system and the details of its operation are illustrated in the different sections of the paper. The performance of the risk assessment model is illustrated by evaluating a number of control strategies facing different short-term influent conditions as well as long-term variability using the IWA/COST simulation benchmark. The results demonstrate that some control strategies, although performing better regarding operating costs and effluent quality, induce a higher risk for solids separation problems. In view of these results, it is suggested to integrate empirical knowledge into mechanistic models to increase reliability and to allow assessment of potential side-effects when simulating complex processes.     

Joyce—A programming language for distributed systems

This paper describes a secure programming language called Joyce based on CSP and Pascal. Joyce permits unbounded (recursive) activation of communicating agents. The agents exchange messages through synchronous channels. A channel can transfer messages of different types between two or more agents. A compiler can check message types and ensure that agents use disjoint sets of variables only. The use of Joyce is illustrated by a variety of examples.     

A new functional expansion for nonlinear systems

In this paper, we shall introduce a new functional series expansion of the output of a general nonlinear system as power series in powers of derivatives of the input components. We shall illustrate the usefulness of this series by considering two important problems namely those of “realization” and systems inversion.     

Analysis of NUMA effects in modern multicore systems for the design of high-performance data transfer applications

To capitalize on multicore power, modern high-speed data transfer applications usually adopt multi-threaded design and aggregate multiple network interfaces. However, NUMA introduces another dimension of complexity to these applications. In this paper, we undertook comprehensive experiment on real systems to illustrate the importance of NUMA-awareness to applications with intensive memory accesses and network I/Os. Instead of simply attributing the NUMA effect to the physical layout, we provide an in-depth analysis of underlying interactions inside hardware devices. We profile the system performance by monitoring relevant hardware counters, and reveal how the NUMA penalty occurs during prefetch and cache synchronization processes. Consequently, we implement a thread mapping module in a bulk data transfer software, BBCP, as a practical example of enabling NUMA-awareness. The enhanced application is then evaluated on our high-performance testbed with storage area networks (SAN). Our experimental results show that the proposed NUMA optimizations can significantly improve BBCP’s performance in memory-based tests with various contention levels and realistic data transfers involving SAN-based storage.     

A study on product modelling for integration of CAD/CAM

Modelling capability for products to be designed and manufactured plays an important role in order to effectively construct and utilize CAD/CAM systems. Product models should represent all the information about products, which is utilized in manufacturing processes. Therefore it is required that they describe functional structures of machine products, and include not only geometric information but also various non-geometric data, such as physical, technological and management data. Presently there do not seem to exist definite methods or theories for constructing product models. In this paper, we first investigate the whole manufacturing process, and propose a system structure for integration of CAD/CAM, in which product modelling plays a fundamental role. Then requirements for product modelling are studied thoroughly, and a new representation framework for product models is proposed. It consists of an object concept called frame, relations among frames and attributes, and it can incorporate the existing various modelling capabilities, such as solid modelling. We use this representation framework in combination with our solid modelling package GEOMAP-III, and show the effectiveness of this approach by performing illustrative design experiments.     

Experiment selection for the discrimination of semi-quantitative models of dynamical systems

Modeling an experimental system often results in a number of alternative models that are all justified by the available experimental data. To discriminate among these models, additional experiments are needed. Existing methods for the selection of discriminatory experiments in statistics and in artificial intelligence are often based on an entropy criterion, the so-called information increment. A limitation of these methods is that they are not well-adapted to discriminating models of dynamical systems under conditions of limited measurability. Moreover, there are no generic procedures for computing the information increment of an experiment when the models are qualitative or semi-quantitative. This has motivated the development of a method for the selection of experiments to discriminate among semi-quantitative models of dynamical systems. The method has been implemented on top of existing implementations of the qualitative and semi-quantitative simulation techniques QSIM, Q2, and Q3. The applicability of the method to real-world problems is illustrated by means of an example in population biology: the discrimination of four competing models of the growth of phytoplankton in a bioreactor. The models have traditionally been considered equivalent for all practical purposes. Using our model discrimination approach and experimental data we show, however, that two of them are superior for describing phytoplankton growth under a wide range of experimental conditions.     

Use of modelling error dynamics for the calibration of water distribution systems

The calibration of a water distribution system remains a complex task. Leakages create additional difficulties if their proportion is high. Not all leakages are real. A part of them are apparent leakages. An optimization procedure is proposed to obtain the proportions of real and apparent leakages. The distribution of the leakages over a WDS may be made easier by analyzing the dependence of modelling errors on water flow. It is suggested that pressure measurements should be ranked according to the closeness of measurement to the water source and that modelling errors should be analysed separately for the measurements of different order (from the first to the highest). The advantage of this approach is that the analysis is confined within points of measurements of one order and it allows to redistribute fluxes of leakages (real and apparent) more reliably. Nonlinear optimization with linear constraints is used for the redistribution of leakages over a WDS. The methodology proposed allows to exclude the influence of systematic errors on the result of calibration.     

Input addition and leader selection for the controllability of graph-based systems

In this paper, we consider dynamical graph-based models, which are well fitted for the structural analysis of complex systems. A significant amount of work has been devoted to the controllability of such graph based models, e.g. recently for multi-agent systems or complex networks. We study here the controllability through input addition in this framework. We present several variants of this problem depending on the freedom which is left to the designer on the additional inputs. We use a unified framework, which allows us to encompass the different applications and representations (large scale systems, complex communications networks, multi-agent systems, …) and provide convenient graph tools for their analysis. Our contribution is to characterize the structural modifications of the system resulting from an input addition (or a leader selection) and of the mechanisms which lead to controllability. We provide information on the possible location of additional inputs and on the minimal number of inputs to be added for controllability.     

Adaptive low-gain integral control of linear systems with input and output nonlinearities

An adaptive low-gain integral control framework is developed for tracking constant reference signals in a context of finite-dimensional, exponentially stable, single-input, single-output linear systems with positive steady-state gain and subject to locally Lipschitz, monotone input and output nonlinearities of a general nature: the input nonlinearity is required to satisfy an asymptotic growth condition (of sufficient generality to accommodate nonlinearities ranging from saturation to exponential growth) and the output nonlinearity is required to satisfy a sector constraint in those cases wherein the input nonlinearity is unbounded.     

Improving the efficiency of direct-memory-access output operations

The improvements in throughput expected from direct-memory-access channels are not realized with simple input/output routines. For output operations, the improvements can be realized through the provision of a separate data buffer area in memory that may be accessed by the peripheral while the primary buffer area is being manipulated by the program. The method may be extended to peripherals that require error checking after output.