This paper looks at a new method of modelling non-l inear dynamic processes, using grid-type Sugeno fuzzy models and a priori knowledge. The proposed hybrid fuzzy convolution dynamic model consists of a non-linear fuzzy steady-state static, and a gain-independent impulse response model-based dynamic part. The modelling of non-linear pH processes is chosen as a realistic case study for the demon...

This paper presents the design and implementation of a systematic fuzzy modelling methodology for the inverse dynamic modelling of robot manipulators. The fuzzy logic modelling methodology is motivated in part by the difficulties encountered in the modelling of complex nonlinear uncertain systems, and by the objective of developing an efficient dynamic model for the real-time model-based contro...

The SNE special issues on Object-oriented and Structural-dynamic Modelling and Simulation emphasize on recent developments in languages and tools for object-oriented modelling of complex systems and on approaches, languages and tools for structural-dynamic systems. Computer aided modelling and simulation of complex systems , using components from multiple application domains, have in recent yea...

Agent modelling is a challenging problem in many modern artificial intelligence applications. The agent modelling task is especially difficult when handling stochastic choices, deliberately hidden information, dynamic agents, and the need for fast learning. State estimation techniques, such as Kalman filtering and particle filtering, have addressed many of these challenges, but have received li...

Agent modelling is a challenging problem in many modern artificial intelligence applications. The agent modelling task is especially difficult when handling stochastic choices, deliberately hidden information, dynamic agents, and the need for fast learning. State estimation techniques, such as Kalman filtering and particle filtering, have addressed many of these challenges, but have received li...

Bayesian time series and forecasting is a very broad field and any attempt at other than a very selective and personal overview of core and recent areas would be foolhardy. This chapter therefore selectively notes some key models and ideas, leavened with extracts from a few time series analysis and forecasting examples. For definitive development of core theory and methodology of Bayesian state...

We introduce the Xdπ calculus, a peer-to-peer model for reasoning about dynamic web data. Web data is not just stored statically. Rather it is referenced indirectly, for example using hyperlinks, service calls, or scripts for dynamically accessing data, which require the complex coordination of data and processes between sites. The Xdπ calculus models this coordination, by integrating the XML d...

This paper is about modelling dynamic dependencies of components as required in dynamic environments. We sketch a formal model for describing the dependencies of software components on hardware and other software components. In a unified way, we represent software components and hardware components with their properties. The properties can be changed during runtime. Expressions over properties ...

This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic an...