Collaborating Authors

Tenth Anniversary of the Plastics Color Formulation Tool

AI Magazine

Since 1994, GE Plastics has employed a case-based reasoning (CBR) tool that determines color formulas that match requested colors. This tool, called FormTool, has saved GE millions of dollars in productivity and material (that is, colorant) costs. The technology developed in FormTool has been used to create an online color-selection tool for our customers called ColorXpress Select. A customer innovation center has been developed around the FormTool software. In offices and factories, in hospitals, homes, and schools, on the road and in outer space, products made with GE materials make life simpler, safer, and more comfortable for people every day.

? ???? ???AI?? ? ????????????????????? ? ??? RaspberryPi?????? ? ?? ?????(UCAVs) ??????? "ALPHA"??? Psibernetix??? ?? ????? - Qiita


PsiberLogic is a completely free, open-source fuzzy logic controller package for Python 3. Psibernetix proudly supports the amazing Python community, and is happy to contribute to Python's open-source movement. This package is for anyone seeking a high-performance, python3-callable package for creating fuzzy logic controllers. Details on ALPHA – a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering (Computational Intelligence): Nikola K. Kasabov: 9780262112123: Books


The author has performed an excellent job in explaining the fundamental ideas and practical methods of different AI techniques. AI problems in the field ( pattern recognition, speech and image processing, classification, planning, optimization, control, time-series and analogy-based prediction, diagnosis, decision making and game simulations) are discussed and illustrated with examples . Especially useful are the comparisons between different techniques (AI rule Cbased methods, fuzzy methods, connectionist methods, and hybrid systems for knowledge engineering) used to solve the same or similar problems. The presented text is suitable for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering.The book¡ s appendices summarize data sets for the examples in the book. All data sets are available through anonymous FTP.

On revising fuzzy belief bases Artificial Intelligence

We look at the problem of revising fuzzy belief bases, i.e., belief base revision in which both formulas in the base as well as revision-input formulas can come attached with varying truth-degrees. Working within a very general framework for fuzzy logic which is able to capture a variety of types of inference under uncertainty, such as truth-functional fuzzy logics and certain types of probabilistic inference, we show how the idea of rational change from 'crisp' base revision, as embodied by the idea of partial meet revision, can be faithfully extended to revising fuzzy belief bases. We present and axiomatise an operation of partial meet fuzzy revision and illustrate how the operation works in several important special instances of the framework.

Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering Artificial Intelligence

Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). We develop the notion of double-boundary fuzzy granules and elaborate on its implications. Type 1 and type 2 fuzzy inference systems can be obtained from the projection of Fuzzy eIX granules. We perform the principle of the balanced information granularity within Fuzzy eIX classifiers to achieve a higher level of model understandability. Internal and external granules are updated from a numerical data stream at the same time that the global granular structure of the classifier is autonomously evolved. A synthetic nonstationary problem called Rotation of Twin Gaussians shows the behavior of the classifier. The Fuzzy eIX classifier could keep up with its accuracy in a scenario in which offline-trained classifiers would clearly have their accuracy drastically dropped.