Development of a Crisp Fuzzy-Like Controller Using Formula-Based and Vectorized Approaches

Simplifying of implementation of linear state feedback fuzzy controllers is investigated through the thesis. One of the most important problems in fuzzy controller design is the number of fuzzy subsets (membership functions) for each fuzzy input/output variable. The number of fuzzy subsets and consequently the number of fuzzy rules should be big enough to achieve good approximation of control surface and have a smooth and robust control. However as the number of rules increases, the memory space, and program cycle time and total project cost will also increase dramatically. The thesis proposes crisp-fuzzy like controller derived by two novel approaches. The first one which is formula based crisp fuzzy-like controller proves that the monotonic fuzzy controller is similar to nonlinear saturated controller and then represents several different controller formulas. The second controller namely vectorized crisp fuzzy -like controller maps the fuzzy variables in a vectorial space and derives formula that has the structure similar to PID controllers. The proposed controllers are inspired from fuzzy logic where they can express the control law semantically but they are absolutely crips.Consenquently the needed memory space is minimizes since the rule table has been replace with the formula. On the other fuzzy controllers have high computational complexity while the new controllers are very simple to design, tune and implment.some new performance indexes also are porposed to evaluate the performance and stability of different controllers. Several well-known industrial models are used for simulation and a dimmer circuit to control the bulb temperature,has been used as a case study. Both simulation and experimental results show that the crips - fuzzy like controllers have the same or in some cases better performance and stability compare with the conventional fuzzy logic controllers, with extra merits of lower memory space and cycle time

Bibliometric Mapping of the Computational Intelligence Field

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation

Application of Computational Intelligence Techniques to Process Industry Problems

In the last two decades there has been a large progress in the computational intelligence research field. The fruits of the effort spent on the research in the discussed field are powerful techniques for pattern recognition, data mining, data modelling, etc. These techniques achieve high performance on traditional data sets like the UCI machine learning database. Unfortunately, this kind of data sources usually represent clean data without any problems like data outliers, missing values, feature co-linearity, etc. common to real-life industrial data. The presence of faulty data samples can have very harmful effects on the models, for example if presented during the training of the models, it can either cause sub-optimal performance of the trained model or in the worst case destroy the so far learnt knowledge of the model. For these reasons the application of present modelling techniques to industrial problems has developed into a research field on its own. Based on the discussion of the properties and issues of the data and the state-of-the-art modelling techniques in the process industry, in this paper a novel unified approach to the development of predictive models in the process industry is presented

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available