Data and models are needed to analyse the dynamic behaviour of any system. One of the main goals of mathematicalmodelling is optimization of some objective function subject to certain constraints. Nature has an optim...
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Data and models are needed to analyse the dynamic behaviour of any system. One of the main goals of mathematicalmodelling is optimization of some objective function subject to certain constraints. Nature has an optimality principle in biological systems. For example shapes of different cells or bones in the human body are optimal for the function they perform. Optimization models may provide better understanding in diagnosis of diseases as well as in patient health care systems. The paper deals with application of the optimization technique of dynamic programming to model the human gall bladder.
Buildings and structures adjacent to railways are subjected to vibration generated in the track and transmitted through the ground. These vibration phenomena have been researched for conventional railway tracks and th...
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Buildings and structures adjacent to railways are subjected to vibration generated in the track and transmitted through the ground. These vibration phenomena have been researched for conventional railway tracks and the effect on human subjects well understood. Railways or tramways built in city streets are usually much closer to buildings than conventional railways. Therefore the generation and transmission of vibration is more critical in tramway tracks. This paper examines work undertaken to identify the mechanism for generating vibration at the rail head, and applies it to the new LR55 track system. A model is created to determine the path of vibrations from the rail head through the new LR55 track structure and into the ground. Knowing the vibrations which are transmitted into the ground enables an assessment to be made of the impacts on adjacent buildings.
We describe a new method for performing automated mathematicalmodelling for financial time series prediction using fuzzy logic techniques, dynamical systems and fractal theory. The main idea is that using fuzzy logic...
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We describe a new method for performing automated mathematicalmodelling for financial time series prediction using fuzzy logic techniques, dynamical systems and fractal theory. The main idea is that using fuzzy logic techniques we can simulate and automate the reasoning process of human experts in mathematicalmodelling for financial time series prediction. Our new method for automated modelling consists of three main parts: time series analysis, developing a set of admissible models, and selecting the "best" model. Our method for time series analysis consists of using the fractal dimension of a set of points as a measure of the geometrical complexity of the time series. Our method for developing a set of admissible dynamical systems models is based on the use of fuzzy logic techniques to simulate the decision process of the human experts in modelling financial problems. The selection of the "best" model for financial time series prediction (FTSP) is done using heuristics from the experts and statistical calculations. This new method can be implemented as a computer program and can be considered an intelligent system for automated mathematicalmodelling for FTSP.
Multi layer perceptrons (MLPs) can be applied as a tool to model human classification behaviour. In the present theoretical study we attempt to interpret MLPs within the framework of mathematical psychological models ...
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Multi layer perceptrons (MLPs) can be applied as a tool to model human classification behaviour. In the present theoretical study we attempt to interpret MLPs within the framework of mathematical psychological models for human classification behaviour, more specifically the general recognition theory and the generalized context model. Next, four error criteria are discussed that can be used in training and test of the MLPs, in relation to two types of data representation: in terms of individual deterministic responses or in terms of probabilistic responses. All error measures considered are additive, i.e. can be written as a sum across individual stimuli. It is shown that some of these error measures have very different properties given a training set, and that the interpretation of the MLP as a means to provide knowledge about the underlying human decision process depends on the complexity of the MLP-topology.
A mathematical model for evoked potentials is outlined. The model has been developed by considering phase synchronisation of the underlying neurological processes. The model, consisting of an ensemble of uncoupled lin...
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A mathematical model for evoked potentials is outlined. The model has been developed by considering phase synchronisation of the underlying neurological processes. The model, consisting of an ensemble of uncoupled linear oscillators with a gaussian distribution of frequencies is shown to reproduce the typical response of P100 visual evoked response. The model structure is parallel in form and is considered to be physiologically realistic. It is also shown that the calculated behaviour of the ensemble can be generated by a 2/sup nd/ order linear differential equation with time varying coefficients, thus highlighting the fact that entirely different physical structures can generate identical responses.
The recent advances in multimedia systems, together with the advent of high speed networks, paved the way to a new generation of applications. In particular, the authoring environments have found in multimedia the mea...
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ISBN:
(纸本)0819420417
The recent advances in multimedia systems, together with the advent of high speed networks, paved the way to a new generation of applications. In particular, the authoring environments have found in multimedia the means of increasing the richness of information contained in electronic documents. With the evolution of new computer systems that can handle multimedia information, time-based data can be integrated in electronic documents taking into account their temporal dimension. In such documents, temporal dependencies between the different media objects define a temporal structure within the document. This structure is the basic support for the representation of dependencies between data such as audio, video and virtual images. Furthermore, it allows the scheduling of presentation actions during the document presentation. The presentation of multimedia documents is dynamic and the positioning of objects in time together with their duration have to be specified. To achieve this operation efficiently, a high level temporal representation is needed which allows the author to specify all the temporal dependencies between multimedia objects. In this paper, we propose an interval-based temporal model and constraints which provide a basis for the management of the consistency of multimedia documents. We propose an efficient algorithm allowing the detection of a wide range of inconsistencies. The emphasis in the design of these algorithms is put on the handling of both the flexibility of temporal specifications and the indeterministic behaviour of some media objects. Furthermore, we use the logical organization of the document in nested entities to enhance the performance of the methods used for detecting inconsistencies. The aim of our approach is to fulfill the following requirements . Structured modelling: The document content is defined by a hierarchy of nested components where leaves are basic media objects and nodes composite objects. . Incremental manipulation of th
This paper presents some results from a project aimed at the development of characteristic patterns of energy use based on structural models of energy consumption. The structure of the models developed allows an analy...
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This paper presents some results from a project aimed at the development of characteristic patterns of energy use based on structural models of energy consumption. The structure of the models developed allows an analysis of demand side management (DSM) impact and future demand. This paper is focused on the application of fuzzy filters in transferring linguistic variables expressed in terms of common words into mathematical operators. These linguistic variables appear when customers describe their behaviour and life styles. The fuzzy filters used not only transfer linguistic variables into computerised forms but they also map uncertainty from linguistic data into filter parameters.
The primary role of the cardiovascular system is to deliver a required amount of oxygen to various organs. These needs vary with time and depend on the vascular site. To maintain a required diffusion level of oxygen i...
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The primary role of the cardiovascular system is to deliver a required amount of oxygen to various organs. These needs vary with time and depend on the vascular site. To maintain a required diffusion level of oxygen in the capillary networks, the cardiovascular system is controlled by regulation processes that maintain pressure and blood flow to the required physiological levels. The authors have developed a nonlinear hemodynamic model associated with a varying elastance model of the heart and four simple control processes acting on the heart rate and the vascular resistances. The computed results, showing variations of arterial mean pressure, heart rate and vascular resistances agree with the experimental data under resting conditions, during moderate exercise of 200 W and during the recuperation phase.
The reason for the present upsurge of interest in intelligent control is that the present generation of control systems are incapable, to a greater or lesser extent, of dealing with problems of a certain complexity. F...
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The reason for the present upsurge of interest in intelligent control is that the present generation of control systems are incapable, to a greater or lesser extent, of dealing with problems of a certain complexity. Fortunately, human operators (HO) are often experts in keeping the complex control systems on the right track. In this paper a method for controller design has been investigated, based on a concept of developing a mathematical model for the behaviour of the HO of the process. This method treats the HO's behaviour as a dynamic process in itself, transformed from the dynamics of the unknown process to be controlled. The following three phases are distinguished in controller design: (i) Observation phase, during which the HO controls the process by himself for a specific time interval. During this time the data representing the state of the process and the corresponding human controller actions are registered. (ii) modelling phase, during which the mapping between the inputs and the outputs of the controller is learned. The model used is a neural network with radial basis functions, and the estimation of the model parameters is conducted by using a multiparameter least-square estimation. (iii) Testing phase, during which the human operator behaviour model or the derived neural controller is used to control the process. The controller performance is evaluated by analysing its behaviour under both the same conditions used in learning phase, and completely different conditions to study the controller reliability. The method has been successfully applied to control a nonlinear level-control process using computer simulation.
The reason for the present upsurge of interest in intelligent control is that the present generation of control systems are incapable, to a greater or less extent, of dealing with problems of a certain complexity. For...
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The reason for the present upsurge of interest in intelligent control is that the present generation of control systems are incapable, to a greater or less extent, of dealing with problems of a certain complexity. Fortunately, human operators are often experts in keeping the complex control systems on the right track. In this paper a method for controller design has been investigated based on a concept of developing a mathematical model for the behaviour of the human operator of the process. This method treats the human operator behaviour as a dynamic process by itself, transformed from the dynamics of the unknown process to be controlled. There are three phases for controller design, observation, modelling and testing phases during which the available observed data are used for training the system and testing it. The method has been successfully applied to control a nonlinear process using computer simulation.
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