@article{altug_trussell_chow_1999, title={A "mutual update" training algorithm for fuzzy adaptive logic control/decision network (FALCON)}, volume={10}, ISSN={["1045-9227"]}, DOI={10.1109/72.737508}, abstractNote={The conventional two-stage training algorithm of the fuzzy/neural architecture called FALCON may not provide accurate results for certain type of problems, due to the implicit assumption of independence that this training makes about parameters of the underlying fuzzy inference system. In this correspondence, a training scheme is proposed for this fuzzy/neural architecture, which is based on line search methods that have long been used in iterative optimization problems. This scheme involves synchronous update of the parameters of the architecture corresponding to input and output space partitions and rules defining the underlying mapping; the magnitude and direction of the update at each iteration is determined using the Armijo rule. In our motor fault detection study case, the mutual update algorithm arrived at the steady-state error of the conventional FALCON training algorithm as twice as fast and produced a lower steady-state error by an order of magnitude.}, number={1}, journal={IEEE TRANSACTIONS ON NEURAL NETWORKS}, author={Altug, S and Trussell, HJ and Chow, MY}, year={1999}, month={Jan}, pages={196–199} }
 @article{altug_chow_trussell_1999, title={Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis}, volume={46}, ISSN={["0278-0046"]}, DOI={10.1109/41.807988}, abstractNote={Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault detection/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN/FZ) inference systems, namely, fuzzy adaptive learning control/decision network (FALCON) and adaptive network based fuzzy inference system (ANFIS), with applications to induction motor fault detection/diagnosis problems. The general specifications of the NN/FZ systems are discussed. In addition, the fault detection/diagnosis structures are analyzed and compared with regard to their learning algorithms, initial knowledge requirements, extracted knowledge types, domain partitioning, rule structuring and modifications. Simulated experimental results are presented in terms of motor fault detection accuracy and knowledge extraction feasibility. Results suggest new and promising research areas for using NN/FZ inference systems for incipient fault detection and diagnosis in induction motors.}, number={6}, journal={IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS}, author={Altug, S and Chow, MY and Trussell, HJ}, year={1999}, month={Dec}, pages={1069–1079} }
 @article{chow_altug_trussell_1999, title={Heuristic constraints enforcement for training of and knowledge extraction from a fuzzy/neural architecture - Part I: Foundation}, volume={7}, DOI={10.1109/91.755396}, abstractNote={Using fuzzy/neural architectures to extract heuristic information from systems has received increasing attention. A number of fuzzy/neural architectures and knowledge extraction methods have been proposed. Knowledge extraction from systems where the existing knowledge limited is a difficult task. One of the reasons is that there is no ideal rulebase, which can be used to validate the extracted rules. In most of the cases, using output error measures to validate extracted rules is not sufficient as extracted knowledge may not make heuristic sense, even if the output error may meet the specified criteria. The paper proposes a novel method for enforcing heuristic constraints on membership functions for rule extraction from a fuzzy/neural architecture. The proposed method not only ensures that the final membership functions conform to a priori heuristic knowledge, but also reduces the domain of search of the training and improves convergence speed. Although the method is described on a specific fuzzy/neural architecture, it is applicable to other realizations, including adaptive or static fuzzy inference systems. The foundations of the proposed method are given in Part I. The techniques for implementation and integration into the training are given in Part II, together with applications.}, number={2}, journal={IEEE Transactions on Fuzzy Systems}, author={Chow, M.-Y. and Altug, S. and Trussell, H. J.}, year={1999}, pages={143–150} }
 @article{altug_chow_trussell_1999, title={Heuristic constraints enforcement for training of and rule extraction from a fuzzy/neural architecture - Part II: Implementation and application}, volume={7}, ISSN={["1063-6706"]}, DOI={10.1109/91.755397}, abstractNote={For part I, see ibid., p.143-50. This paper is the second of two companion papers. The foundations of the proposed method of heuristic constraint enforcement on membership functions for knowledge extraction from a fuzzy/neural architecture was given in Part I. Part II develops methods for forming constraint sets using the constraints and techniques for finding acceptable solutions that conform to all available a priori information Moreover, methods of integration of enforcement methods into the training of the fuzzy-neural architecture are discussed. The proposed technique is illustrated on a fuzzy-AND classification problem and a motor fault detection problem. The results indicate that heuristic constraint enforcement on membership functions leads to extraction of heuristically acceptable membership functions in the input and output spaces. Although the method is described on a specific fuzzy/neural architecture, it is applicable to any realization of a fuzzy inference system, including adaptive and/or static fuzzy inference systems.}, number={2}, journal={IEEE TRANSACTIONS ON FUZZY SYSTEMS}, author={Altug, S and Chow, MY and Trussell, HJ}, year={1999}, month={Apr}, pages={151–159} }