This chapter gives an overview of system identification techniques for fuzzy models and some selected techniques for model-based fuzzy control. It starts with a brief discussion of the position of fuzzy modelling within the general nonlinear identification setting. The two most commonly used fuzzy models are the Mamdani model and the Takagi-Sugeno model. An overview of techniques for the data-driven construction of the latter model is given. We discuss both structure selection (input variables, representation of dynamics, number and type of membership functions) and parameter estimation (local and global estimation techniques, weighted least squares, multi-objective optimisation). Further, we discuss control design based on a fuzzy model of the process. As the model is assumed to be obtained through identification from sampled data, we focus on discrete-time methods, including: gain-scheduling and state-feedback design, model-inverse control and predictive control. A real-world application example is given.

Systems engineers are increasingly faced with problems for which traditional tools and techniques are ill suited. Such problems are posed by systems that are complex, uncertain and not conducive to deterministic analysis. Modern tools and techniques are being applied in these subject areas in order to address such problems. This chapter introduces the multi-objective genetic algorithm (MOGA) of Fonseca and Fleming [1] as one of the tools for addressing these problems. Background to genetic algorithms (GAs) is given in terms of their operators and abstraction of the problem domain. Multi-objective optimisation is introduced using the concept of Pareto dominance and trade-off between competing objectives. Visualisation techniques are illustrated in which 'many-objective' problems mabe handled and preference articulation maybe implemented. The motivation for using GAs for multi-objective problems such as control systems design and systems identification is given. The chapter concludes with case studies illustrating the use of MOGA for control system design and multi objective genetic programming (MOGP) for system identification.

The local linear modelling approach is capable of modelling complex nonlinear processes. The transparency of the model architecture permits the incorporation of prior knowledge in different ways and the integration of process knowledge and experimental data has been demonstrated in detail for the modelling of the heat exchanger. Utilising an arbitrary local modelling strategy does not automatically guarantee a transparent model. The employed LOLIMOT algorithm has proven to yield transparent and well interpretable models. The appropriate choice of an excitation signal is vital for a good model. Here, the transparency of the local structure again aids the engineer in the design and the assessment of the excitation signal. The interpretability of the local structure offers simple ways for the validation of the identified model based on local characteristic values as process gains or time constants. The obtained local linear models can further be utilised in model-based control approaches as well as process monitoring or fault detection.

Neuro-fuzzy model uses a set of fuzzy rules to model unknown dynamical systems with a B-spline functional. T-S inference mechanism is useful to produce an operating point dependent structure facilitating the use of state-space models for data fusion or data-based controller design. This chapter aims to introduce some neuro-fuzzy model construction, design and estimation algorithms that are developed to deal with fundamental problems in data modelling, such as model sparsity, robustness, transparency and rule-based learning process. Some work on ASMOD that has been derived based on ANOVA are reviewed initially and then a locally regularised orthogonal least-squares algorithm, based on T-S and ANOVA and combined with a D-optimality used for subspace-based rule selection, has been proposed for fuzzy rule regularisation and subspace-based information extraction.

Current control methodologies can generally be divided into model based and model free. The first contains conventional controllers, the second so-called intelligent controllers. Conventional control designs involve constructing dynamic models of the target system and the use of mathematical techniques to derive the required control law. Therefore, when a mathematical model is difficult to obtain, either due to complexity or the numerous uncertainties inherent in the system, conventional techniques are less useful. Intelligent control may offer a useful alternative in this situation.

In this chapter, selected aspects of the fault diagnosis problem for control systems have been considered with special attention paid to fault detection and, to a lesser extent, fault isolation. In particular, the robustness problem of FDI with respect to the requirement to maximise sensitivity to faults while minimising (or decoupling) the effect of uncertain effects through unknown inputs has been described. This severe robustness challenge accompanying analytical methods of FDI has led to the requirement of developing new methodologies for FDI in which analytical and CI techniques are combined to achieve good global and nonlinear modelling and robustness. The chapter has outlined some of the recent research on FDI and fault diagnosis for dynamic systems, using this integrated approach. The examples have shown that by using evolutionary computing, neural networks and NF modelling structures realistic solutions are achievable. It is hoped that this direction of research will stimulate an increased adoption of real industrial application to make mode-based FDI more usual and effective in real process systems.

The first part of the chapter describes the results of two literature surveys on intelligent systems allied to medicine which were commissioned by the European Networks of Excellence ERUDIT and EUNITE. The first survey covered the use of fuzzy technology across the whole of medicine divided into ten sub-disciplines, ranking from surgery to many branches of internal medicine. The second survey covered the field of intelligent adaptive systems in medicine, again applied to the same ten subdivisions as above, but reduced to five sub-areas in the analysis of the results. In this case a wide range of intelligent techniques was considered including fuzzy inference, neural networks and model-based reasoning. Both surveys attempted to show the merits and limitation of the current work and to give signposts for future developments in the field. The second part of the chapter contains two relevant case studies. The first concerns intelligent systems in anaesthesia monitoring and control. The theme is unconsciousness management in operating theatres, which is a particularly challenging application for AI techniques. The second case study concerns the developments of nonlinear models based on ANN and neuro-fuzzy techniques for both classification and prediction in cancer survival studies. The particular application is that of bladder tumour prognosis using gene expression markers together with demographic data and social conditions such as smoking.