Number Of Learning Techniques Research Articles

Transformative Theory in Social and Organizational Research

In social and organizational research, theory is conventionally used to explain social phenomena. However, theory may be transformative in the sense that in using and testing the theory in a practical domain, researchers may attempt to help practitioners transform and improve their social practices and institutions. This idea is illustrated by a research-and-development project in Denmark, headed by the author, which used transformative theory to design professional conferences that are more conducive to participant learning and involvement than is the conventional, lecture-based format. A number of learning techniques were derived from the theory and were tested as hypotheses: When implemented in thirty live conference experiments, did they contribute to learning, as specified by the theory? Used in this manner, transformative theory may supplement the aspirations motivating change agents by some of the well-known qualities of scientific research, including theoretical grounding, a coherent ontology, testable hypotheses, systematic evaluation, external validity, and theory–action consistency.

Fuzzy learning and applications

From the Book: The resurgence of interest in fuzzy logic over the past few decades has opened many new avenues in its applications. The most notable examples include autofocus cameras, automobile transmissions, air conditioners, aerospace, subway systems, robots, business, and medicine. Fuzzy logic leads to greater generality and better rapport with reality. It is driven by the need for methods of analysis and design which can come to grips with the pervasive imprecision of the real world and exploit the tolerance for imprecision to achieve tractability, robustness, and low cost solution. It also provides effective capability of processing uncertainties to model human thinking. Artificial neural networks and genetic algorithms can be fused with fuzzy logic to provide the learning abilities in machines. This book presents a sample of research on fuzzy learning and their applications by notable researchers. It contains 11 chapters. The first chapter, by Russo, is on evolutionary fuzzy learning. It uses the fusion of neural networks and genetic algorithms for fuzzy supervised learning. It is demonstrated that this approach is superior for tasks such as classification, function approximation, and time series prediction. In chapter 2, Vidal-Verdu, Navas, Delgado-Restituto, and RodriguezVazquez present a stored-programmable mixed-signal fuzzy controller chip with supervised learning capabilities. The proposed architecture consists of a programmable analog core which implement the active rule, a RAM memory for storing global rule set, and a selection circuitry for the identification of subregions. The third chapter, by Botfa, Barbers, and Skasmeta, is on fuzzy modeling in a multi-agent framework for learning in autonomous systems. A number of learning techniques are proposed in the intelligent agents environment. The fourth chapter, by Masulli and Sperduti, is on learning techniques for supervised fuzzy classifiers. A family of learning machines for supervised classification is developed using the neuro-fuzzy systems. The fifth chapter, by Yeh and Chen, is on multistage fuzzy control. A general method to generate fuzzy rules for a multistage fuzzy controller from the performance index of the control system is presented. The simulation results using three-stage fuzzy inference showed that good performance is achieved using this approach. The sixth chapter, by Lotfi, is on learning fuzzy systems. This chapter presents a review of techniques available for updating/ learning the parameters of a fuzzy system. In the seventh chapter, Tachibana, Furuhashi, Shimoda, Kawakami, and Fukunaga present an application of fuzzy modeling to the analysis of rowing boat speed. The fuzzy modeling is applied to reveal the relationships between the supplied power and the boat speed. The eighth chapter, by Cavalieri and Russo, is on a novel fuzzy approach to Hopfield coefficients determination. It is shown that the proposed method features a search for the set of coefficients which guarantees valid solutions very close to the global minima of the energy function, or ones which guarantee robust solution. The ninth chapter, by Fortuna, Muscato, Caponetto, and Xibilia is on fuzzy control of a CD player focusing system. The fuzzy controller is a classical Sugeno type Fuzzy-FD. A practical implementation on a real system using a fuzzy microcontroller validates the superiority of this approach. The tenth chapter, by Ali, Ghafoor, and Lee, is on a neuro-fuzzy scheduler for a multimedia web server. The problem of multimedia synchronization in a web environment is considered. The results are validated and found satisfactory. The last chapter, by Leski and Henzel, is on a neuro-fuzzy system based on logical interpretation of if-then rules. The application of the proposed artificial neural network on standard pattern recognition problems is demonstrated. We believe that this book will be of great value to researchers, scientists and engineers alike. The research students will receive an in-depth tutorial on the topics covered. The seasoned researcher will appreciate the applications and the gold mine of other possibilities for novel research topics. We are grateful to the authors for their contributions. We also thank reviewers for their expertise and time, and CRC Press for their excellent editorial assistance. M. Russo, Italy L.C. Jain, Australia