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Journal articles on the topic 'Fuzzy systems; Neural networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.

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Any fuzzy system is a knowledge‐based system which implies an inference engine. Proposes neural networks as a means of performing the inference. Using the Theorem of Representation proposes an encoding scheme that allows the neural network to be trained to perform modus ponens.

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2

Rutkowska, Danuta, and Yoichi Hayashi. "Neuro-Fuzzy Systems Approaches." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (1999): 177–85. http://dx.doi.org/10.20965/jaciii.1999.p0177.

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Two major approaches to neuro-fuzzy systems are distinguished in the paper. The previous one refers to fuzzy neural networks, which are neural networks with fuzzy signals, and/or fuzzy weights, as well as fuzzy transfer functions. The latter approach concerns neuro-fuzzy systems in the form of multilayer feed-forward networks, which differ from standard neural networks, because elements of particular layers conduct different operations than standard neurons. These structures are neural network representations of fuzzy systems and they are also called connectionist models of fuzzy systems, adap

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3

Godjevac, Jelena, and Nigel Steele. "Fuzzy Systems and Neural Networks." Intelligent Automation & Soft Computing 4, no. 1 (1998): 27–37. http://dx.doi.org/10.1080/10798587.1998.10750719.

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4

Kosko, Bart, and John C. Burgess. "Neural Networks and Fuzzy Systems." Journal of the Acoustical Society of America 103, no. 6 (1998): 3131. http://dx.doi.org/10.1121/1.423096.

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5

Zambelli, Stefano. "Neural networks and fuzzy systems." Journal of Economic Dynamics and Control 17, no. 3 (1993): 523–29. http://dx.doi.org/10.1016/0165-1889(93)90010-p.

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6

Dvorak, V. "Neural networks and fuzzy systems." Knowledge-Based Systems 6, no. 3 (1993): 179. http://dx.doi.org/10.1016/0950-7051(93)90043-s.

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7

Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.

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Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks

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8

Blake, J. "The implementation of fuzzy systems, neural networks and fuzzy neural networks using FPGAs." Information Sciences 112, no. 1-4 (1998): 151–68. http://dx.doi.org/10.1016/s0020-0255(98)10029-4.

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9

NASRABADI, EBRAHIM, and S. MEHDI HASHEMI. "ROBUST FUZZY REGRESSION ANALYSIS USING NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (2008): 579–98. http://dx.doi.org/10.1142/s021848850800542x.

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Some neural network related methods have been applied to nonlinear fuzzy regression analysis by several investigators. The performance of these methods will significantly worsen when the outliers exist in the training data set. In this paper, we propose a training algorithm for fuzzy neural networks with general fuzzy number weights, biases, inputs and outputs for computation of nonlinear fuzzy regression models. First, we define a cost function that is based on the concept of possibility of fuzzy equality between the fuzzy output of fuzzy neural network and the corresponding fuzzy target. Nex

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10

OH, SUNG-KWUN, DONG-WON KIM, and WITOLD PEDRYCZ. "HYBRID FUZZY POLYNOMIAL NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 03 (2002): 257–80. http://dx.doi.org/10.1142/s0218488502001478.

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We propose a hybrid architecture based on a combination of fuzzy systems and polynomial neural networks. The resulting Hybrid Fuzzy Polynomial Neural Networks (HFPNN) dwells on the ideas of fuzzy rule-based computing and polynomial neural networks. The structure of the network comprises of fuzzy polynomial neurons (FPNs) forming the nodes of the first (input) layer of the HFPNN and polynomial neurons (PNs) that are located in the consecutive layers of the network. In the FPN (that forms a fuzzy inference system), the generic rules assume the form "if A then y = P(x) " where A is fuzzy relation

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11

Gao, X. Z., and S. J. Ovaska. "Neural networks-based approximation of fuzzy systems." Integrated Computer-Aided Engineering 10, no. 4 (2003): 319–31. http://dx.doi.org/10.3233/ica-2003-10403.

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12

Zhang, J., and A. J. Morris. "Fuzzy neural networks for nonlinear systems modelling." IEE Proceedings - Control Theory and Applications 142, no. 6 (1995): 551–61. http://dx.doi.org/10.1049/ip-cta:19952255.

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13

MIZRAJI, EDUARDO, and JUAN LIN. "FUZZY DECISIONS IN MODULAR NEURAL NETWORKS." International Journal of Bifurcation and Chaos 11, no. 01 (2001): 155–67. http://dx.doi.org/10.1142/s0218127401002043.

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Modular neural networks structured as associative memories are capable of processing inputs built from tensorial products of vectors. In this context, the operators of propositional and modal logic can be represented as modular distributed memories that can process not only classical Boolean but also fuzzy evaluations of truth-values of sentences. Furthermore, projecting memory outputs onto unit vectors yield discrete dynamical systems that exhibit varying degrees of complexity. As examples, we analyze outcomes of semantic evaluations in several self-referential systems including modal version

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14

HUMPERT, B., and A. DE KORVIN. "Optimization and Expert Systems with Neural Networks." International Journal of Modern Physics C 02, no. 01 (1991): 86–104. http://dx.doi.org/10.1142/s012918319100010x.

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Neural Networks (NN) provide the framework for the optimization of highly complex problems, known as NP-complete. At the same time NN allow in an elegant way for the implementation of forward and backward chaining Expert Systems (ESs) where the knowledge is represented by production rules but non-explicit domain knowledge can also be learnt. The use of fuzzy logic allows for the processing of partial and uncertain information. As a representative example for optimization we discuss the Traveling Salesman problem (TSP) covering also recent progress, and subsequently we focus on the connectionis

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15

HALGAMUGE, SAMAN K., and MANFRED GLESNER. "FUZZY NEURAL NETWORKS: BETWEEN FUNCTIONAL EQUIVALENCE AND APPLICABILITY." International Journal of Neural Systems 06, no. 02 (1995): 185–96. http://dx.doi.org/10.1142/s0129065795000147.

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Research in fuzzy neural networks, which started from application oriented fuzzy system tuning, then moving to the automatic generation of fuzzy systems from data, is reaching a more mature stage, especially after the proof of functional equivalence of certain fuzzy models and neural networks. It is essential that the applicability of such developments is explored emphasizing the directions that research should follow. It can be shown that the nearest prototype classifier is functionally equivalent to an alternative fuzzy classifier model. Efficient, hardware friendly training algorithms are d

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16

Albus, James S., and Alexander M. Meystel. "Intelligent Systems: Beyond Neural Networks and Fuzzy Control." IFAC Proceedings Volumes 29, no. 1 (1996): 8211–16. http://dx.doi.org/10.1016/s1474-6670(17)59016-3.

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17

Adam, J. A. "Bart Kosko [neural networks/fuzzy systems engineer biography]." IEEE Spectrum 33, no. 2 (1996): 58–62. http://dx.doi.org/10.1109/6.482276.

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18

Hayashi, Yoichi, and James J. Buckley. "Approximations between fuzzy expert systems and neural networks." International Journal of Approximate Reasoning 10, no. 1 (1994): 63–73. http://dx.doi.org/10.1016/0888-613x(94)90009-4.

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19

Horikawa, Shin-ichi, Masahiro Yamaguchi, Takeshi Furuhashi, and Yoshiki Uchikawa. "Fuzzy Control for Inverted Pendulum Using Fuzzy Neural Networks." Journal of Robotics and Mechatronics 7, no. 1 (1995): 36–44. http://dx.doi.org/10.20965/jrm.1995.p0036.

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Fuzzy control has a distinctive feature in that it can incorporate experts' control rules using linguistic expressions. The authors have presented various types of fuzzy neural networks (FNNs) called Type I-V. The FNNs can automatically identify the fuzzy rules and tune the membership functions of fuzzy controllers by utilizing the learning capability of neural networks. In particular, the Type IV FNN has a simple structure and can express the identified fuzzy rules linguistically. The authors have also proposed a method to describe the behavior of fuzzy control systems based on the fuzzy mode

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20

Li, S., and M. A. Elbestawi. "Tool Condition Monitoring in Machining by Fuzzy Neural Networks." Journal of Dynamic Systems, Measurement, and Control 118, no. 4 (1996): 665–72. http://dx.doi.org/10.1115/1.2802341.

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The Multiple Principal Component (MPC) Fuzzy Neural Network for tool condition monitoring in machining under varying cutting conditions is proposed. This approach is based on three major components of “soft computation,” namely fuzzy logic, neural network, and probability reasoning. The MPC classification fuzzy neural networks were built through training with learning data obtained from cutting tests performed in a reasonable range of cutting conditions. Several sensors were used for monitoring feature selection. Force, vibration, and spindle motor power signals were fused in multiple principa

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21

Oleinikov, Alexandr Aleksandrovich, and Ilya Aleksandrovich Beresnev. "ASSESSMENT OF ELEMENTS OF DATA-TRANSFER SYSTEMS BY USING FUZZY NEURAL NETWORKS." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2020, no. 4 (2020): 121–31. http://dx.doi.org/10.24143/2072-9502-2020-4-121-131.

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The article considers using direct distribution neural networks and fuzzy neural networks for assessing the operational state of data transmission system elements. In order to select the type of artificial neural network that most fully meets the task of redefining data for predicting the operational state of communication network elements, factors presented in quantitative form are taken into account. For that purpose, the amount of data transmitted through active equipment was selected as the most significant factor having a high level of uncertainty in networks with packet data transmission

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22

Teslyuk, Vasyl, Artem Kazarian, Natalia Kryvinska, and Ivan Tsmots. "Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems." Sensors 21, no. 1 (2020): 47. http://dx.doi.org/10.3390/s21010047.

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In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of arti

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23

GUPTA, M. M., and P. MUSILEK. "FUZZY NEURAL NETWORKS AND COGNITIVE MODELING." International Journal of General Systems 29, no. 1 (2000): 7–28. http://dx.doi.org/10.1080/03081070008960922.

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24

LEE, KEON-MYUNG, DONG-HOON KWANG, and HYUNG LEEK WANG. "A FUZZY NEURAL NETWORK MODEL FOR FUZZY INFERENCE AND RULE TUNING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 03 (1994): 265–77. http://dx.doi.org/10.1142/s0218488594000213.

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It is relatively easy to create rough fuzzy rules for a target system. However, it is time-consuming and difficult to fine-tune them for improving their behavior. Meanwhile, in the process of fuzzy inference the defuzzification operation takes most of the inferencing time. In this paper, we propose a fuzzy neural network model which makes it possible to tune fuzzy rules by employing neural networks and reduces the burden of defuzzification operation. In addition, to show the applicability of the proposed model we perform an experiment and present its result.

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25

Keller, James M., and Hossein Tahani. "Backpropagation neural networks for fuzzy logic." Information Sciences 62, no. 3 (1992): 205–21. http://dx.doi.org/10.1016/0020-0255(92)90016-2.

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26

PARK, BYOUNG-JUN, WITOLD PEDRYCZ, and SUNG-KWUN OH. "SIMPLIFIED FUZZY INFERENCE RULE-BASED GENETICALLY OPTIMIZED HYBRID FUZZY NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 02 (2008): 245–74. http://dx.doi.org/10.1142/s0218488508005169.

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In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Netwo

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27

Abiyev, Rahib H. "Credit Rating Using Type-2 Fuzzy Neural Networks." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/460916.

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Nowadays various new technologies such as artificial neural networks, genetic algorithms, and decision trees are used for modelling of credit rating. This paper presents design of credit rating model using a type-2 fuzzy neural networks (FNN). In the paper, the structure of the type-2 FNN is designed and its learning algorithm is derived. The proposed network is constructed on the base of a set of fuzzy rules that includes type-2 fuzzy sets in the antecedent part and a linear function in the consequent part of the rules. A fuzzy clustering algorithm and gradient learning algorithm are implemen

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28

ZANCHETTIN, CLEBER, LEANDRO L. MINKU, and TERESA B. LUDERMIR. "DESIGN OF EXPERIMENTS IN NEURO-FUZZY SYSTEMS." International Journal of Computational Intelligence and Applications 09, no. 02 (2010): 137–52. http://dx.doi.org/10.1142/s1469026810002823.

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Interest in hybrid methods that combine artificial neural networks and fuzzy inference systems has grown in recent years. These systems are robust solutions that search for representations of domain knowledge, reasoning on uncertainty, automatic learning and adaptation. However, the design and definition of the parameter effectiveness of such systems is still a hard task. In the present work, we perform a statistical analysis to verify interactions and interrelations between parameters in the design of neuro-fuzzy systems. The analysis is carried out using a powerful statistical tool, namely,

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29

Negoita, Mircea. "Microtechnology in Bucharest Fuzzy Systems and Neural Networks Laboratory." Journal of Japan Society for Fuzzy Theory and Systems 7, no. 1 (1995): 70–72. http://dx.doi.org/10.3156/jfuzzy.7.1_70.

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30

Reyneri, L. M. "Unification of neural and wavelet networks and fuzzy systems." IEEE Transactions on Neural Networks 10, no. 4 (1999): 801–14. http://dx.doi.org/10.1109/72.774224.

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31

Spooner, J. T., and K. M. Passino. "Stable adaptive control using fuzzy systems and neural networks." IEEE Transactions on Fuzzy Systems 4, no. 3 (1996): 339–59. http://dx.doi.org/10.1109/91.531775.

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32

Harrington, Peter B. "Fuzzy multivariate rule-building expert systems: Minimal neural networks." Journal of Chemometrics 5, no. 5 (1991): 467–86. http://dx.doi.org/10.1002/cem.1180050506.

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33

Mantas, C. J., and J. M. Puche. "Artificial Neural Networks are Zero-Order TSK Fuzzy Systems." IEEE Transactions on Fuzzy Systems 16, no. 3 (2008): 630–43. http://dx.doi.org/10.1109/tfuzz.2007.902016.

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34

Cios, Krzysztof. "Machine learning — Neural networks, genetic algorithms, and fuzzy systems." Neurocomputing 8, no. 2 (1995): 223–24. http://dx.doi.org/10.1016/0925-2312(95)90033-0.

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35

Li, Hongxing. "Fuzzy logic systems are equivalent to feedforward neural networks." Science in China Series E: Technological Sciences 43, no. 1 (2000): 42–54. http://dx.doi.org/10.1007/bf02917136.

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36

Saneifard, R. "Some properties of neural networks in designing fuzzy systems." Neural Computing and Applications 21, S1 (2011): 215–20. http://dx.doi.org/10.1007/s00521-011-0777-1.

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37

Vasilevskij, V. V., and M. O. Poliakov. "Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems." Electrical Engineering & Electromechanics, no. 1 (February 23, 2021): 10–14. http://dx.doi.org/10.20998/2074-272x.2021.1.02.

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Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by spe

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38

Suzuki, Ikuo, Keitaro Naruse, and Yukinori Kakazu. "A Study on Adaptive Navigation System using Nonlinear Neural Networks(Neural Fuzzy Application,Session: MP2-C)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 36. http://dx.doi.org/10.1299/jsmeicam.2004.4.36_1.

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39

Sung-Kwun Oh, W. Pedrycz, and Ho-Sung Park. "Genetically optimized fuzzy polynomial neural networks." IEEE Transactions on Fuzzy Systems 14, no. 1 (2006): 125–44. http://dx.doi.org/10.1109/tfuzz.2005.861620.

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40

BANAKAR, AHMAD, MOHAMMAD FAZLE AZEEM, and VINOD KUMAR. "COMPARATIVE STUDY OF WAVELET BASED NEURAL NETWORK AND NEURO-FUZZY SYSTEMS." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 06 (2007): 879–906. http://dx.doi.org/10.1142/s0219691307002099.

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Based on the wavelet transform theory and its well emerging properties of universal approximation and multiresolution analysis, the new notion of the wavelet network is proposed as an alternative to feed forward neural networks and neuro-fuzzy for approximating arbitrary nonlinear functions. Earlier, two types of neuron models, namely, Wavelet Synapse (WS) neuron and Wavelet Activation (WA) functions neuron have been introduced. Derived from these two neuron models with different non-orthogonal wavelet functions, neural network and neuro-fuzzy systems are presented. Comparative study of wavele

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41

Ramanna, Sheela. "C++ neural networks and fuzzy logic." Control Engineering Practice 3, no. 8 (1995): 1199–200. http://dx.doi.org/10.1016/0967-0661(95)90103-5.

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42

BROWN, M., and C. J. HARRIS. "A PERSPECTIVE AND CRITIQUE OF ADAPTIVE NEUROFUZZY SYSTEMS USED FOR MODELLING AND CONTROL APPLICATIONS." International Journal of Neural Systems 06, no. 02 (1995): 197–220. http://dx.doi.org/10.1142/s0129065795000159.

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This paper outlines some of the theoretical and practical developments being made in neurofuzzy systems. As the name suggests, neurofuzzy networks were developed by fusing the ideas that originated in the fields of neural and fuzzy systems. A neurofuzzy network attempts to combine the transparent, linguistic, symbolic representation associated with fuzzy logic with the architecture and learning rules commonly used in neural networks. These hybrid structures have both a qualitative and a quantitative interpretation and can overcome some of the difficulties associated with solely neural algorith

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43

Bakievna Khuzyatova, Lyalya, and Lenar Ajratovich Galiullin. "Optimization of parameters of neuro-fuzzy model." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (2020): 229. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp229-232.

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<p>The need for increasing the efficiency of the neuron-fuzzy model in the formation of knowledge bases is being updated. The task is to develop methods and algorithms for presetting and optimizing the parameters of a fuzzy neural network. To solve difficult formalized tasks, it is necessary to develop decision support systems - expert systems based on a knowledge base. ES developers are constantly faced with the problems of “extraction” and formalization of knowledge, as well as the search for new ways to obtain it. To do this, use the extraction, acquisition and formation of knowledge.

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44

Thammano, Arit, and Sirinda Palahan. "Time-Series Forecasting Using Fuzzy-Neural System with Evolutionary Rule Base." Journal of Robotics and Mechatronics 18, no. 5 (2006): 672–79. http://dx.doi.org/10.20965/jrm.2006.p0672.

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This paper proposes a new hybrid time-series forecasting system which is the fusion of fuzzy systems and artificial neural networks. The proposed fuzzy-neural system consists of 5 layers: an input layer, fuzzification layer, rule layer, hidden layer, and output layer. The artificial neural network is used as the fuzzy inference engine and the genetic algorithm is used to optimize the fuzzy rule base. This proposed system was tested with 20 time-series datasets. The results obtained were very encouraging.

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45

YAMAGUCHI, T., T. TAKAGI, and T. MITA. "Self-organizing control using fuzzy neural networks." International Journal of Control 56, no. 2 (1992): 415–39. http://dx.doi.org/10.1080/00207179208934321.

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46

Araújo Júnior, José M., Leandro L. S. Linhares, Fábio M. U. Araújo, and Otacílio M. Almeida. "Fuzzy wavelet neural networks applied as inferential sensors of neonatal incubator dynamics." Journal of Intelligent & Fuzzy Systems 39, no. 3 (2020): 2567–79. http://dx.doi.org/10.3233/jifs-190129.

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Newborns with health complications have great difficulty in regulating the body temperature due to distinct factors, which include the high metabolism rate and low weight. In this context, neonatal incubators help maintaining good health conditions because they provide a thermally-neutral environment, which is adequate to ensure the least energy expenditure by the newborn. In the last decades, artificial neural networks (ANNs) have been established as one of the main tools for the identification of nonlinear systems. Among the various approaches used in the identification process, the fuzzy wa

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47

Lin, Cheng-Jian. "SISO Nonlinear System Identification Using a Fuzzy-Neural Hybrid System." International Journal of Neural Systems 08, no. 03 (1997): 325–37. http://dx.doi.org/10.1142/s0129065797000331.

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This paper describes a fuzzy-neural hybrid system for the identification of nonlinear dynamic systems with unknown parameters. The proposed model takes the form of a context-sensitive module in which a fuzzy system is used as a function module and a multilayer neural network is used as a context module. Fuzzy-neural hybrid systems with a decomposed structures reduce complexity and thus accelerate the learning process. Also, the parameters of a fuzzy system have clear physical meanings, which makes it possible to incorporate a priori knowledge into the selection of initial parameter values and

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48

Bibi, Youssouf, Omar Bouhali, and Tarek Bouktir. "Hybrid fuzzy direct/indirect adaptive controller for uncertain nonlinear systems." Transactions of the Institute of Measurement and Control 42, no. 15 (2020): 3012–23. http://dx.doi.org/10.1177/0142331220939728.

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This paper describes a new approach to adaptive control of uncertain nonlinear systems. A fuzzy logic controller is used to combine both direct and indirect methods. Based on the fuzzy neural networks, the plant unknown nonlinear functions are estimated, and then combined to form the indirect control law. In parallel, another fuzzy neural network approximates the direct adaptive control. According to the modelling error and its derivatives, the fuzzy logic controller modulates between direct and indirect adaptive controllers. The global stability of the overall system is shown by constructing

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49

Akram, Muhammad, Ather Ashraf, and Mansoor Sarwar. "Novel Applications of Intuitionistic Fuzzy Digraphs in Decision Support Systems." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/904606.

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Many problems of practical interest can be modeled and solved by using graph algorithms. In general, graph theory has a wide range of applications in diverse fields. In this paper, the intuitionistic fuzzy organizational and neural network models, intuitionistic fuzzy neurons in medical diagnosis, intuitionistic fuzzy digraphs in vulnerability assessment of gas pipeline networks, and intuitionistic fuzzy digraphs in travel time are presented as examples of intuitionistic fuzzy digraphs in decision support system. We have also designed and implemented the algorithms for these decision support s

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50

Mazzatorta, P., E. Benfenati, C. D. Neagu, and G. Gini. "Tuning Neural and Fuzzy-Neural Networks for Toxicity Modeling." Journal of Chemical Information and Computer Sciences 43, no. 2 (2003): 513–18. http://dx.doi.org/10.1021/ci025585q.

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