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INTERNATIONAL JOINT CONF ON NEURAL NETWORKS ( IJCNM-90 )

作者： BURRASCANO, P LUCCI, P Info-Com Dept Univ di Roma 'La Sapienza ' Italy

Convergence problems in the case of the generalized delta rule are discussed. A modification to the nonlinearity of processing elements is proposed which is shown to smooth the cost function to minimized during the learning phase. A variation to the generalized delta rule learning procedure, required by the introduced modification, is discussed. Extensive tests have been performed on several examples proposed in the technical literature. The tests show the effectiveness of the proposed procedure in improving the convergence properties of the backpropagation algorithm. In particular, it was shown that the proposed modification virtually eliminates nonconvergence problems if a moderate η value is used

关键词： backpropagation cost function delta constraining processing elements learning phase generalized delta rule learning procedure convergence properties backpropagation algorithm convergence knowledge based systems learning systems neural nets

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An Analysis of MVL Neural Operators Using Feed Forward backpropagation: Realization and Application of Logic Synthesis 1

An Analysis of MVL Neural Operators Using Feed Forward Backp...

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International Conference on Smart Smart Sensors and Applications

作者： Chowdhury, Adib Kabir Razali, Md. Saifullah Wyai, Gary Loh Chee Gopal, Lenin Madon, Bakri Singh, Ashutosh Kumar Univ Coll Technol Sarawak Sch Comp Sibu Malaysia Curtin Univ Sarawak Dept Elect & Comp Engn Miri Malaysia NIT Dept Comp Applicat Kurukshetra Haryana India

ISBN: (纸本)9781479973644

In this paper, a Neural Network Deployment (NND) algorithm is presented to realize and synthesize Multi-Valued Logic (MVL) functions. The algorithm is combined with back-propagation learning capability and MVL operators. The operators are used to synthesize the functions. Consequently the synthesized expressions are applied by the MVL neural operators. The advantages of NND-MVL algorithm are demonstrated by accuracy measurement of MVL neural operator realization. Furthermore, evaluation of NND-MVL algorithm is analyzed by its application, propagation delay and accuracy achieved for training with 4 hidden neurons. In a brief, an effort of training MVL neural operators and utilizing them for logic synthesis is observed.

关键词： MVL functions learning capability backpropagation algorithm synthesis of functions neural network

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Neural Network with Lower and Upper Type-2 Fuzzy Weights using the backpropagation Learning Method

Neural Network with Lower and Upper Type-2 Fuzzy Weights usi...

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Joint World Congress of the International-Fuzzy-Systems-Association (IFSA) / Annual Meeting of the North-American-Fuzzy-Information-Processing-Society (NAFIPS)

作者： Gaxiola, Fernando Melin, Patricia Valdez, Fevrier Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781479903481

In this paper the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis in the adaptation of these methods with type-2 fuzzy weights. In this paper, we work with type-2 fuzzy weights lower and upper in the neural network architecture and the lower and upper final results obtained are presented in the final. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system

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backpropagation Learning Method with Interval Type-2 Fuzzy Weights in Neural Networks

Backpropagation Learning Method with Interval Type-2 Fuzzy W...

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International Joint Conference on Neural Networks (IJCNN)

作者： Gaxiola, Fernando Melin, Patricia Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781467361293;9781467361286

In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this paper the neural network architecture managing lower and upper type-2 fuzzy weights and the obtained lower and upper final results are presented. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system

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An Error backpropagation-based Background Calibration of Pipeline TI-ADCs for 256-QAM Optical Coherent Receivers 12

An Error Backpropagation-based Background Calibration of Pip...

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12th IEEE Latin America Symposium on Circuits and System (LASCAS)

作者： Bocco, Alvaro Fernandez Solis, Fredy Reyes, Benjamin T. Morero, Damian A. Hueda, Mario R. Fdn Fulgor RA-5000 Cordoba Argentina Univ Nacl Cordoba Lab Comunicac Digitales CONICET RA-5000 Cordoba Argentina

ISBN: (纸本)9781728176703

A novel background calibration technique for the residue amplifier gain of a pipeline time-interleaved analog to digital converter (TI-ADC) is proposed in this paper. The technique is based on the backpropagation algorithm, which is widely used in neural network training, and can be applied to any pipeline topology, regardless of the number of stages. backpropagation-based calibration is applicable to digital communication receivers, and the particular application discussed in this paper is coherent optical transceivers. The technique is able to operate in conjunction with other backpropagation-based TI-ADC mismatch calibration algorithms without introducing additional complexities. The main advantages of the technique proposed here are its robustness, its speed of convergence and the fact that it always works in background mode, which makes it suitable to track any process, voltage and temperature variations. Simulations are reported to demonstrate the effectiveness of the proposal for two different pipeline ADC topologies.

关键词： backpropagation algorithm gain calibration optical coherent receiver pipeline TI-ADC residue amplifier

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Telecommunication Fraud Prediction Using backpropagation Neural Network

Telecommunication Fraud Prediction Using Backpropagation Neu...

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International Conference of Soft Computing and Pattern Recognition

作者： Mohamed, Azlinah Bandi, Ahmad Fuad Mohamed Tamrin, Abdul Razif Jaafar, Md Daud Hasan, Suriah Jusof, Faeizah Univ Teknol MARA Fac Math & Comp Sci Shah Alam Selangor Malaysia TM Innovat Ctr Telekom Res & Dev Sdn Bhd Cyberjaya Malaysia

ISBN: (纸本)9781424453306

Telecommunication fraud prediction in this paper is an interpolation problem that uses the daily telecommunication network services information to analyze and predict the alarm information generated from a detection engine by minimizing false cases. This paper proposed the use of backpropagation neural network (BPNN) to perform telecommunication interpolation based on local telecommunication network services. The backpropagation algorithm is adjusted accordingly to control the speed of obtaining solution. The validation of this method is carried out following a series of experiments to establish the suitable BPNN parameters value for this interpolation problem. 20,000 cases were used and divided suitably into training and testing samples in analyzing the performance of the BPNN network. It is observed that the performance of BPNN in predicting fraud was merely 100% if the threshold value was set to 0.64 and above the predicted value. Consequently, the network model created could be used to analyze fraud risk classification and subsequently, provide contribution to the domain of fraud detection system.

关键词： Fraud detection system Telecommunication fraud backpropagation algorithm Call data records fraud risk clasification

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Comparison of backpropagation and Kalman Filter-based Training for Neural Networks 25

Comparison of Backpropagation and Kalman Filter-based Traini...

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25th International Conference on System Theory, Control and Computing (ICSTCC)

作者： Luttmann, Laurin Mercorelli, Paolo Leuphana Univ Lueneburg Inst Prod & Proc Innovat Univ Alle 1 D-21335 Luneburg Germany

ISBN: (纸本)9781665414968

This work describes and compares the backpropagation algorithm with the extended Kalman filter (EKF), a second-order training method which can be applied to the problem of learning neural network parameters and is known to converge in only a few iterations. The algorithms are compared with respect to their effectiveness and speed of convergence using simulated data for both, a regression and a classification task.

关键词： backpropagation algorithm Kalman Filter Neural Networks

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CFD investigation and ANN prediction of heat transfer coefficient for fully developed turbulent air flow around double V-baffle turbulators

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Case Studies in Thermal Engineering 2025年 71卷

作者： Abdulaziz Alasiri H.E. Fawaz Department of Mechanical Engineering College of Engineering Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 13318 Saudi Arabia Department of Mechanical Engineering National Research Centre Giza Egypt

This paper presents a numerical study of periodic fully developed turbulent airflow in a rectangular channel with double upstream V-baffles installed on the upper and lower walls of the channel in an in-line manner. Utilizing the OpenFOAM open-source software, this numerical research investigates the impact of Re from 10,000 to 40,000 and BR from 0.3 to 0.5 on flow structure and heat transfer performance. An ANN model is constructed to estimate the local heat transfer coefficient using results obtained from the current CFD simulations and utilizing axial local distance (X/P), Re, and BR as ANN input parameters. The process of training incorporates the analysis of the loss function on training and validation data for controlling the weights and biases using backpropagation while feed forward propagate the selected input parameters. A total of 11 hidden layers consisting of 24 neurons each has been used in constructing the ANN, and the training process is optimized using the ADAM algorithm to minimize the loss function. The Final layer uses the linear activation function while all the hidden layers use the rectified Linear Units Activation function (ReLU). The ANN model demonstrates excellent predictive performance, yielding values close to 1 for R 2 and r, along with extremely low values for MSE, MAPE, MSLE, and log-cosh loss (0.01, 0.6 %, 0.001, and 0.01, respectively), demonstrating the ANN model's high predictive accuracy.

关键词： Cross flow Turbulent flow Artificial neural network Multilayer perceptron backpropagation algorithm V-baffle turbulators

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FUZZY REGRESSION-ANALYSIS USING NEURAL NETWORKS

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FUZZY SETS AND SYSTEMS 1992年第3期50卷 257-265页

作者： ISHIBUCHI, H TANAKA, H Department of Industrial Engineering University of Osaka Prefecture Sakai Osaka Japan

In this paper, we propose simple but powerful methods for fuzzy regression analysis using neural networks. Since neural networks have high capability as an approximator of nonlinear mappings, the proposed methods can be applied to more complex systems than the existing LP based methods. First we propose learning algorithms of neural networks for determining a nonlinear interval model from the given input-output patterns. A nonlinear interval model whose outputs approximately include all the given patterns can be determined by two neural networks. Next we show two methods for deriving nonlinear fuzzy models from the interval model determined by the proposed algorithms. Nonlinear fuzzy models whose h-level sets approximately include all the given patterns can be derived. Last we show an application of the proposed methods to a real problem.

关键词： FUZZY REGRESSION FUZZY NONLINEAR MODEL NEURAL NETWORKS SUPERVISED LEARNING backpropagation algorithm

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Proposal of a declarative and parallelizable artificial neural network using the notification-oriented paradigm

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NEURAL COMPUTING & APPLICATIONS 2018年第6期30卷 1715-1731页

作者： Schutz, Fernando Fabro, Joao A. Ronszcka, Adriano F. Stadzisz, Paulo C. Simao, Jean M. UTFPR Fed Univ Technol CPGEI Grad Program Elect Engn & Ind Informat BR-80230901 Curitiba PR Brazil UTFPR Fed Univ Technol PPGCA Grad Program Appl Comp BR-80230901 Curitiba PR Brazil

Since the 1960s, artificial neural networks (ANNs) have been implemented and applied in various areas of knowledge. Most of these implementations had their development guided by imperative programming (IP), usually resulting in highly coupled programs. Thus, even though intrinsically parallel in theory, ANNs do not easily take an effective distribution on multiple processors when developed under IP. As an alternative, the notification-oriented paradigm (NOP) emerges as a new programming technique. NOP facilitates the development of decoupled and distributed systems, using abstraction of knowledge through logical-causal rules, as well as the generation of an optimized code. Both features are possible by means of a notification-oriented inference process, which avoids structural and temporal redundancies in the logic-causal evaluations. These advantages are relevant to systems that have parts decoupled in order to run in parallel, such as ANN. In this sense, this work presents the development of a multilayer perceptron ANN using backpropagation training algorithm based on the concepts of a NOP implementation. Such implementation allows, transparently from high-level programming, parallel code generation that runs on multicore platforms. Furthermore, the solution based on NOP, when compared against the equivalent on IP, presents a high level of decoupling and explicit use of logic-causal elements, which are, respectively, useful to distribution, understanding and improvement of the application.

关键词： Notification-oriented paradigm Artificial neural network backpropagation algorithm Decoupled systems

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