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Fuzzy Logic, Identification and Predictive Control
Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding "judgement” rather than simple "yes/no”, "on/off” responses: circumstances where a linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose. Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining. This monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
Nonlinear Modeling
Nonlinear Modeling: Advanced Black-Box Techniques discusses methods on Neural nets and related model structures for nonlinear system identification; Enhanced multi-stream Kalman filter training for recurrent networks; The support vector method of function estimation; Parametric density estimation for the classification of acoustic feature vectors in speech recognition; Wavelet-based modeling of nonlinear systems; Nonlinear identification based on fuzzy models; Statistical learning in control and matrix theory; Nonlinear time-series analysis. It also contains the results of the K.U. Leuven time series prediction competition, held within the framework of an international workshop at the K.U. Leuven, Belgium in July 1998.
Meta-Learning in Computational Intelligence
Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explo...
Optimal Real-time Control of Sewer Networks
A sine qua non of control system development for modern sewer networks is the preservation of the water system around a network’s outflow(s). Several approaches have been proposed for the optimisation of sewage control and Optimal Real-time Control of Sewer Networks provides a comparative synthesis of a central sewer network flow control based on two of these: nonlinear-optimal and multivariable-feedback control. Testing and comparison of these protocols are made on the basis of their control results for the large-scale sewer network located around the river Obere Iller in Bavaria. The control strategies implemented within this network are based on this study. From the selection of possible methods of control and moving to the implementation of those methods in a real sewer system, this monograph will be invaluable to control and civil engineers working in sewage flow and wastewater treatment and of interest to academics wishing to see how their ideas on optimal control work out when practically applied.
Piezoelectric Transducers for Vibration Control and Damping
This book presents recent developments in vibration control systems that employ embedded piezoelectric sensors and actuators, reviewing ways in which active vibration control systems can be designed for piezoelectric laminated structures, paying distinct attention to how such control systems can be implemented in real time. Includes numerous examples and experimental results obtained from laboratory-scale apparatus, with details of how similar setups can be built.
Nonlinear Model Based Process Control
The ASI on Nonlinear Model Based Process Control (August 10-20, 1997~ Antalya - Turkey) convened as a continuation of a previous ASI which was held in August 1994 in Antalya on Methods of Model Based Process Control in a more general context. In 1994, the contributions and discussions convincingly showed that industrial process control would increasingly rely on nonlinear model based control systems. Therefore, the idea for organizing this ASI was motivated by the success of the first one, the enthusiasm expressed by the scientific community for continuing contact, and the growing incentive for on-line control algorithms for nonlinear processes. This is due to tighter constraints and constan...
Precision Motion Control
This second edition of Precision Motion Control focuses on enabling technologies for precision engineering. It has been extensively edited and rewritten throughout with the following particular areas being expanded or added: • piezoelectric actuators • fine movement control • gantry-stage control • interpolation of quadrature encoder signals • geometrical error modeling for single-, dual- and general-XY-axis stages.
Optimal Control of Wind Energy Systems
Covering all aspects of this important topic, this work presents a review of the main control issues in wind power generation, offering a unified picture of the issues surrounding its optimal control. Discussion is focused on a global dynamic optimization approach to wind power systems using a set of optimization criteria which comply with a comprehensive group of requirements including: energy conversion efficiency; mechanical reliability; and quality of the energy provided.
