Transductive-Weighted Neuro-fuzzy Inference System for Tool Wear Prediction in a Turning Process

This paper presents the application to the modeling of a novel technique of artificial intelligence. Through a transductive learning process, a neuro-fuzzy inference system enables to create a different model for each input to the system at issue. The model was created from a given number of known data with similar features to data input. The sum of these individual models yields greater accuracy to the general model because it takes into account the particularities of each input. To demonstrate the benefits of this kind of modeling, this system is applied to the tool wear modeling for turning process.This work was supported by DPI2008-01978 COGNETCON and CIT-420000-2008-13 NANOCUT-INT projects of the Spanish Ministry of Science and Innovation.Peer reviewe

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System

The posterity of Zadeh's 50-year-old paper: A retrospective in 101 Easy Pieces – and a Few More

International audienceThis article was commissioned by the 22nd IEEE International Conference of Fuzzy Systems (FUZZ-IEEE) to celebrate the 50th Anniversary of Lotfi Zadeh's seminal 1965 paper on fuzzy sets. In addition to Lotfi's original paper, this note itemizes 100 citations of books and papers deemed “important (significant, seminal, etc.)” by 20 of the 21 living IEEE CIS Fuzzy Systems pioneers. Each of the 20 contributors supplied 5 citations, and Lotfi's paper makes the overall list a tidy 101, as in “Fuzzy Sets 101”. This note is not a survey in any real sense of the word, but the contributors did offer short remarks to indicate the reason for inclusion (e.g., historical, topical, seminal, etc.) of each citation. Citation statistics are easy to find and notoriously erroneous, so we refrain from reporting them - almost. The exception is that according to Google scholar on April 9, 2015, Lotfi's 1965 paper has been cited 55,479 times

An MS Windows prototype for automatic general purpose image-based flaw detection

Flaw detection plays a crucial role in many industries to make sure that the products meet the specified quality requirements. When making for example a car it is important that all the parts satisfy certain quality standards to make sure the consumer buys a car that is safe to operate. A crack or another weakness in a crucial part can be catastrophic. To make sure their cars are as safe as possible, car manufacturers are conducting thorough testing of crucial parts. Similar tests are done in a wide variety of industries, and these quality controls are often referred to as flaw detection. Any cracks, voids, or other weaknesses that can cause danger are called flaws. Flaw detection is often done, or preferred done, in real time-- in an assembly line fashion. An important constraint, in addition to reliability, is therefore speed. The techniques used in these tests varies. Common techn~ques are ultrasonic waves (1-D or 2-D), eddy current imaging, x-ray imaging, thermal imaging, and fluorescent penetrent imaging. In this thesis I will discuss automatic general purpose image-based flaw detection. Automatic means that the flaw detection is performed without human supervision, and general purpose means that the inspection is not tailored to a specific task (i.e. one particular flaw in one particular type of object), but is ideally applicable to any detection problem

Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines

153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale

On the Synthesis of fuzzy neural systems.

by Chung, Fu Lai.Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 166-174).ACKNOWLEDGEMENT --- p.iiiABSTRACT --- p.ivChapter 1. --- Introduction --- p.1Chapter 1.1 --- Integration of Fuzzy Systems and Neural Networks --- p.1Chapter 1.2 --- Objectives of the Research --- p.7Chapter 1.2.1 --- Fuzzification of Competitive Learning Algorithms --- p.7Chapter 1.2.2 --- Capacity Analysis of FAM and FRNS Models --- p.8Chapter 1.2.3 --- Structure and Parameter Identifications of FRNS --- p.9Chapter 1.3 --- Outline of the Thesis --- p.9Chapter 2. --- A Fuzzy System Primer --- p.11Chapter 2.1 --- Basic Concepts of Fuzzy Sets --- p.11Chapter 2.2 --- Fuzzy Set-Theoretic Operators --- p.15Chapter 2.3 --- "Linguistic Variable, Fuzzy Rule and Fuzzy Inference" --- p.19Chapter 2.4 --- Basic Structure of a Fuzzy System --- p.22Chapter 2.4.1 --- Fuzzifier --- p.22Chapter 2.4.2 --- Fuzzy Knowledge Base --- p.23Chapter 2.4.3 --- Fuzzy Inference Engine --- p.24Chapter 2.4.4 --- Defuzzifier --- p.28Chapter 2.5 --- Concluding Remarks --- p.29Chapter 3. --- Categories of Fuzzy Neural Systems --- p.30Chapter 3.1 --- Introduction --- p.30Chapter 3.2 --- Fuzzification of Neural Networks --- p.31Chapter 3.2.1 --- Fuzzy Membership Driven Models --- p.32Chapter 3.2.2 --- Fuzzy Operator Driven Models --- p.34Chapter 3.2.3 --- Fuzzy Arithmetic Driven Models --- p.35Chapter 3.3 --- Layered Network Implementation of Fuzzy Systems --- p.36Chapter 3.3.1 --- Mamdani's Fuzzy Systems --- p.36Chapter 3.3.2 --- Takagi and Sugeno's Fuzzy Systems --- p.37Chapter 3.3.3 --- Fuzzy Relation Based Fuzzy Systems --- p.38Chapter 3.4 --- Concluding Remarks --- p.40Chapter 4. --- Fuzzification of Competitive Learning Networks --- p.42Chapter 4.1 --- Introduction --- p.42Chapter 4.2 --- Crisp Competitive Learning --- p.44Chapter 4.2.1 --- Unsupervised Competitive Learning Algorithm --- p.46Chapter 4.2.2 --- Learning Vector Quantization Algorithm --- p.48Chapter 4.2.3 --- Frequency Sensitive Competitive Learning Algorithm --- p.50Chapter 4.3 --- Fuzzy Competitive Learning --- p.50Chapter 4.3.1 --- Unsupervised Fuzzy Competitive Learning Algorithm --- p.53Chapter 4.3.2 --- Fuzzy Learning Vector Quantization Algorithm --- p.54Chapter 4.3.3 --- Fuzzy Frequency Sensitive Competitive Learning Algorithm --- p.58Chapter 4.4 --- Stability of Fuzzy Competitive Learning --- p.58Chapter 4.5 --- Controlling the Fuzziness of Fuzzy Competitive Learning --- p.60Chapter 4.6 --- Interpretations of Fuzzy Competitive Learning Networks --- p.61Chapter 4.7 --- Simulation Results --- p.64Chapter 4.7.1 --- Performance of Fuzzy Competitive Learning Algorithms --- p.64Chapter 4.7.2 --- Performance of Monotonically Decreasing Fuzziness Control Scheme --- p.74Chapter 4.7.3 --- Interpretation of Trained Networks --- p.76Chapter 4.8 --- Concluding Remarks --- p.80Chapter 5. --- Capacity Analysis of Fuzzy Associative Memories --- p.82Chapter 5.1 --- Introduction --- p.82Chapter 5.2 --- Fuzzy Associative Memories (FAMs) --- p.83Chapter 5.3 --- Storing Multiple Rules in FAMs --- p.87Chapter 5.4 --- A High Capacity Encoding Scheme for FAMs --- p.90Chapter 5.5 --- Memory Capacity --- p.91Chapter 5.6 --- Rule Modification --- p.93Chapter 5.7 --- Inference Performance --- p.99Chapter 5.8 --- Concluding Remarks --- p.104Chapter 6. --- Capacity Analysis of Fuzzy Relational Neural Systems --- p.105Chapter 6.1 --- Introduction --- p.105Chapter 6.2 --- Fuzzy Relational Equations and Fuzzy Relational Neural Systems --- p.107Chapter 6.3 --- Solving a System of Fuzzy Relational Equations --- p.109Chapter 6.4 --- New Solvable Conditions --- p.112Chapter 6.4.1 --- Max-t Fuzzy Relational Equations --- p.112Chapter 6.4.2 --- Min-s Fuzzy Relational Equations --- p.117Chapter 6.5 --- Approximate Resolution --- p.119Chapter 6.6 --- System Capacity --- p.123Chapter 6.7 --- Inference Performance --- p.125Chapter 6.8 --- Concluding Remarks --- p.127Chapter 7. --- Structure and Parameter Identifications of Fuzzy Relational Neural Systems --- p.129Chapter 7.1 --- Introduction --- p.129Chapter 7.2 --- Modelling Nonlinear Dynamic Systems by Fuzzy Relational Equations --- p.131Chapter 7.3 --- A General FRNS Identification Algorithm --- p.138Chapter 7.4 --- An Evolutionary Computation Approach to Structure and Parameter Identifications --- p.139Chapter 7.4.1 --- Guided Evolutionary Simulated Annealing --- p.140Chapter 7.4.2 --- An Evolutionary Identification (EVIDENT) Algorithm --- p.143Chapter 7.5 --- Simulation Results --- p.146Chapter 7.6 --- Concluding Remarks --- p.158Chapter 8. --- Conclusions --- p.159Chapter 8.1 --- Summary of Contributions --- p.160Chapter 8.1.1 --- Fuzzy Competitive Learning --- p.160Chapter 8.1.2 --- Capacity Analysis of FAM and FRNS --- p.160Chapter 8.1.3 --- Numerical Identification of FRNS --- p.161Chapter 8.2 --- Further Investigations --- p.162Appendix A Publication List of the Candidate --- p.164BIBLIOGRAPHY --- p.16

Expert system and fuzzy technique approaches to landslide hazard mapping

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Métodos clásicos y de soft-computing en la optimización de procesos complejos: Aplicación a un proceso de fabricación

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, febrero de 201