There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed. >

https://ir.nctu.edu.tw:443/bitstream/11536/2013/1/A1995QJ99800003.pdf

Neuro-fuzzy modeling and control

Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as int ...

An Introduction to Fuzzy Control

1. Introduction. 2. Basic Concepts of Fuzzy Logic. 3. Fuzzy Sets. 4. Fuzzy Relations, Fuzzy Graphs, and Fuzzy Arithmetic. 5. Fuzzy If-Then Rules. 6. Fuzzy Implications and Approximate Reasoning. 7. Fuzzy Logic and Probability Theory. 8. Fuzzy Logic in Control Engineering. 9. Hierarchical Intelligent Control. 10. Analytical Issues in Fuzzy Logic Control. 11. Fuzzy Logic and Artificial Intelligence. 12. Fuzzy Logic in Database Management and Information Systems. 13. Fuzzy Logic in Pattern Recognition. 14. Fuzzy Model Identification. 15.Advanced Topics of Fuzzy Model Identification. 16.Neuro-Fuzzy Systems. 17. Genetic Algorithms and Fuzzy Logic. References. Index.

Fuzzy Logic: Intelligence, Control, and Information

This two part paper proposes an intelligent energy management agent (IEMA) for parallel hybrid vehicles. IEMA incorporates a driving situation identification component whose role is to assess the driving environment, the driving style of the driver and the operating mode of the vehicle using long and short term statistical features of the drive cycle. This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power split strategy, which is shown to lead to enhanced fuel economy and reduced emissions. In Part I, the overall architecture of IEMA is presented and the driving situation identification process is described. It is specifically shown that a learning vector quantization (LVQ) network can effectively determine the driving condition using a limited duration of driving data. The overall performance of the system under a range of drive cycles is discussed in the second part of this paper.

Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process

Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook

Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this industrial applications of fuzzy logic and intelligent systems. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.

Industrial Applications of Fuzzy Logic and Intelligent Systems

This paper develops a new approach to building Sugeno-type models. The essential idea is to separate the premise identification from the consequence identification, while these are mutually related in the previous methods. A fuzzy discretization technique is suggested to determine the premise of the model, and an orthogonal estimator is provided to identify the consequence of the model. The orthogonal estimator can provide information about the model structure, or which terms to include in the model, and final parameter estimates in a very simple and efficient manner. The well-known gas furnace data of Box and Jenkins is used to illustrate the proposed modeling approach and to compare its performance with other statistical and fuzzy modeling approaches. It shows that the performance of the new approach compares favorably with these existing techniques.

Reza Langari

Papers

Fuzzy Logic: Intelligence, Control, and Information

Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process

Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook

Industrial Applications of Fuzzy Logic and Intelligent Systems

Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques