Nazmul Siddique

Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of the fuzzy controller is then described and finally an evolutionary algorithm is applied to the neurally-tuned-fuzzy controller in which the sigmoidal function shape of the neural network is determined.The important issue of stability is addressed and the text demonstrates empirically that the developed controller was stable within the operating range. The text concludes with ideas for future research to show the reader the potential for further study in this area.Intelligent Control will be of interest to researchers from engineering and computer science backgrounds working in the intelligent and adaptive control.

Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of the fuzzy controller is then described and finally an evolutionary algorithm is applied to the neurally-tuned-fuzzy controller in which the sigmoidal function shape of the neural network is determined.The important issue of stability is addressed and the text demonstrates empirically that the developed controller was stable within the operating range. The text concludes with ideas for future research to show the reader the potential for further study in this area.Intelligent Control will be of interest to researchers from engineering and computer science backgrounds working in the intelligent and adaptive control.

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapterPresents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systemsConsiders real world problems in the domain of systems modelling, control and optimizationContains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

This new edition continues to focus on the nuts and bolts of wireless network access for devices on board vehicles. It has been updated to reflect on the most recent trends in the broad domain of Intelligent Transport Systems. It covers 802.11ac – a recent standard that is very useful in context where a large amount of information is to be sent in a limited time window. The new edition includes a thorough revision of the 'Vehicular Communication: Issues and Standards' chapter, with new citations and a new subsection on security. The new edition also cites numerous fresh research works to give readers an updated overview of the field. An update on the time delay incurred by applications that always run in the background (Skype, etc) is also covered. The 'Future Directions and Research Ideas' chapter is also largely re-written. An entirely new chapter on D2D communication keeping in view the vehicular context has been added in this edition. This volume will be a useful addition to the libraries for both the students of wireless communication and those studying applied probability.

This new edition continues to focus on the nuts and bolts of wireless network access for devices on board vehicles. It has been updated to reflect on the most recent trends in the broad domain of Intelligent Transport Systems. It covers 802.11ac – a recent standard that is very useful in context where a large amount of information is to be sent in a limited time window. The new edition includes a thorough revision of the 'Vehicular Communication: Issues and Standards' chapter, with new citations and a new subsection on security. The new edition also cites numerous fresh research works to give readers an updated overview of the field. An update on the time delay incurred by applications that always run in the background (Skype, etc) is also covered. The 'Future Directions and Research Ideas' chapter is also largely re-written. An entirely new chapter on D2D communication keeping in view the vehicular context has been added in this edition. This volume will be a useful addition to the libraries for both the students of wireless communication and those studying applied probability.

Focusing on the nuts and bolts of wireless network access for computers on board vehicles, this volume shows how in-car computerization now does much more than merely act as a glorified map-reader. Wireless communication is transforming road travel in ways previously undreamt of, allowing vehicles to “talk” to a wider network and monitor road conditions, potential delays and traffic congestion, all automatically. Toll payments can be made without opening the driver’s window on a cold day, while vehicles might themselves take active steps to avoid collisions. It is the connection between on-board computers and wireless access points, ubiquitous in most cities now, that is a key area of research. Moving vehicles transfer their communications to new points as they progress, and this causes delays, known as “handover latency”. In this book, new stochastic models are developed to map the disruption when connecting to 802.11 WLAN points. It details the application of stochastic tools to analyzing communication networks, as well as previous literature on handover latency and relevant mathematical modeling. Finally, it presents a scheme for monitoring traffic congestion using WLAN connectivity. This volume will be a useful addition to the libraries both of wireless communication students and those studying probability theory.