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检索条件"主题词=Backpropagation Algorithms"

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Blind Separation Algorithm for Audio Signal Based on Genetic Algorithm and Neural Network

Blind Separation Algorithm for Audio Signal Based on Genetic...

引用

International Symposium on Information Science and Engineering, ISISE

作者： Dahui Li Ming Diao Xuefeng Dai Compute and Control Engineering Institute Qiqihar University Qiqihar Heilongjiang China College of Information and Communication Engineering Harbin Engineering of Technology Harbin Heilongjiang China

The blind separation of audio signal is an important application of blind signal separation technology. The traditional separation algorithm based on neural network is analyzed first in this article. The shortage of it is easy to fall into local minimum, and it causes the limitation of convergence slowly and separation results inaccurate. Then, a separation algorithm is designed with genetic algorithm and neural network. The algorithm will optimize the initial value of the weight of separation, in order to control the sample by select operation, or cross operation, or mutation operation in the entire search space. Experiments show that it can obtain optimal values for separation matrix, and the speed of audio signal blind separation is quick and effective obviously.

关键词： Genetic algorithms Neural networks Signal processing algorithms Algorithm design and analysis backpropagation algorithms Genetic engineering Blind source separation Radar signal processing Convergence Source separation

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Adaptive scheduling utilizing a neural network structure

Adaptive scheduling utilizing a neural network structure

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yeou-Fang Wang J.B. Cruz J.H. Mulligan JPL(formerly with UCI) Pasadena CA USA College of Engineering Ohio State Uinversity Columbus OH USA Electrical & Computer Engineering University of California Irvine Irvine CA USA

Adaptive scheduling for flexible manufacturing systems using a neural network structure is described. Adaptive scheduling consists of three parts: features representation of the job orders and the factory environment, a set of fixed scheduling algorithms, and an algorithm selector. The statistics on orders is represented by a small number of feature parameters. Each of the algorithms included in the algorithm set is most suitable for a specific set of orders and factory status, but no single algorithm is best under all conditions. The algorithm selector chooses one of the algorithms to optimize some scheduling performance measure, for given values of the feature parameters. To illustrate the performance of the adaptive scheduling process, 10,000 different order cases were taken from uniformly distributed data. For the simulation, the algorithm selector was implemented using a backpropagation neural network structure. The results of the computer experiments demonstrated the superiority of adaptive scheduling over all of the seven fixed scheduling strategies.< >

关键词： Adaptive scheduling Neural networks Scheduling algorithm backpropagation algorithms Production facilities Job shop scheduling Processor scheduling Flexible manufacturing systems Statistics Computational modeling

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A neural adaptive beamforming system to reduce interfering signals in mobile communications

A neural adaptive beamforming system to reduce interfering s...

引用

SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： E.J.M. Arruda Filho G.P.S. Cavalcante Centro Federal de Educação Tecnológica do Pará-CEFET-PA Departamento de Engenharia Elétrica do Centra Tecnológico Universidade Federal do Pará Belem PA Brazil

This work proposes a neural adaptive beamforming system combining a feedforward artificial neural network with a backpropagation learning algorithm and linear, planar and circular antenna arrays. It intends to reduce interfering signals of the type for multi-path, co-channel interference or near-far field problems in communication systems. The system as a whole maximizes the signal/interference rate (SIR) by the orientation control of the radiation pattern of the arrays. The array individual element excitation allows the creation of a radiation pattern orienting the beam in different directions, pointing out a null (or minimum) for the interfering signal, and a maximum for the desired one.

关键词： Adaptive systems Array signal processing Adaptive arrays Linear antenna arrays Antenna radiation patterns Artificial neural networks backpropagation algorithms Antenna feeds Interchannel interference Position control

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MultiLearner Based Recursive Supervised Training

MultiLearner Based Recursive Supervised Training

引用

IEEE Conference on Cybernetics and Intelligent Systems

作者： Kiruthika Ramanathan Sheng Uei Guan Laxmi R Iyer Department of Electrical and Computer Engineering National University of Singapore Singapore

In supervised learning, most single solution neural networks such as constructive backpropagation give good results when used with some datasets but not with others. Others such as probabilistic neural networks (PNN) fit a curve to perfection but need to be manually tuned in the case of noisy data. Recursive percentage based hybrid pattern training (RPHP) overcomes this problem by recursively training subsets of the data, thereby using several neural networks. MultiLearner based recursive training (MLRT) is an extension of this approach, where a combination of existing and new learners are used and subsets are trained using the weak learner which is best suited for this subset. We observed that empirically, MLRT performs considerably well as compared to RPHP and other systems on benchmark data with 11% improvement in accuracy on the spam dataset and comparable performances on the vowel and the two-spiral problems

关键词： Neural networks backpropagation algorithms Supervised learning Neurons Clustering algorithms Genetics Testing Interpolation Machine learning Unsupervised learning

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Manhattan rule training for memristive crossbar circuit pattern classifiers

Manhattan rule training for memristive crossbar circuit patt...

引用

IEEE International Symposium on Intelligent Signal Processing

作者： Elham Zamanidoost Farnood M. Bayat Dmitri Strukov Irina Kataeva Electrical and Computer Engineering Department University Of California Santa Barbara Santa Barbara CA USA Advanced Research Division Denso Corporation Komenoki-cho Nisshin Japan

We investigated batch and stochastic Manhattan Rule algorithms for training multilayer perceptron classifiers implemented with memristive crossbar circuits. In Manhattan Rule training, the weights are updated only using sign information of classical backpropagation algorithm. The main advantage of Manhattan Rule is its simplicity, which leads to more compact hardware implementation and faster training time. Additionally, in case of stochastic training, Manhattan Rule allows performing all weight updates in parallel, which further speeds up the training procedure. The tradeoff for simplicity is slightly worse classification performance. For example, simulation results showed that classification fidelity on Proben1 benchmark for memristor-based implementation trained with batch Manhattan Rule were comparable to that of classical backpropagation algorithm, and about 2.8 percent worse than the best reported results.

关键词： Training Neurons Hardware Memristors Performance evaluation Classification algorithms backpropagation algorithms

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Anti-Dumping Early-Warning System Based on Neuro-FDT

Anti-Dumping Early-Warning System Based on Neuro-FDT

引用

International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Jian-na Zhao Zhi-peng Chang School of business and Administration North China Electric Power University Baoding China

In this paper, a new anti-dumping early-warning system for the export of China's textile products is presented. The early-warning system based on neuro-fuzzy decision tree modeling method is different from traditional modeling methods. Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are poor in classification accuracy. Neural networks-fuzzy decision tree (a fuzzy decision tree structure with neural like parameter adaptation strategy) improves FDT's classification accuracy and extracts more accuracy human interpretable classification rules. The other new attempt is the setting of early-warning intervals. The result of the positive research indicated that this system is very valid for anti-dumping prediction and it will have a good application prospect in this area

关键词： Decision trees Fuzzy sets Humans Classification tree analysis Textile industry Testing backpropagation algorithms Power system modeling Electronic mail Inspection

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An advanced neural network topology and learning, applied for identification and control of a DC motor

An advanced neural network topology and learning, applied fo...

引用

International IEEE Conference on Intelligent Systems, IS

作者： I.S. Baruch J.M. Flores F. Nara R I.R. Ramirez P B. Nenkova DCA CINVESTAV Mexico CINVESTAV-IPN DCA México D.F México Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional Ciudad de Mexico Ciudad de MÃ©xico MX CINVESTAV-IPN Mexico City Mexico IIT-BAS Sofia Bulgaria

An improved parallel recurrent neural network with canonical architecture, named Recurrent Trainable Neural Network (RTNN), and a normalized error based dynamic backpropagation learning algorithm are analyzed in topics like stability, convergence and rate of convergence, and applied to a D.C. motor identification and control. The theoretical results obtained are given in theorem proof made via Lyapunov function and the unknown nonlinear dynamics of the motor together with the load are identified by the RTNN. The trained RTNN identifier is combined with a reference signal and a RTNN controllers In a direct adaptive control scheme, so In order to achieve a desired trajectory tracking of the motor position. The applicability of the theoretical study is illustrated by experimental results.

关键词： Neural networks Network topology DC motors Recurrent neural networks Convergence Error correction Heuristic algorithms backpropagation algorithms Algorithm design and analysis Stability analysis

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Recurrent Fuzzy Neural Network Using Genetic Algorithm for Linear Induction Motor Servo Drive

Recurrent Fuzzy Neural Network Using Genetic Algorithm for L...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： F. J. Lin P. K. Huang Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

A recurrent fuzzy neural network (RFNN) using genetic algorithm (GA) is proposed to control the mover of a linear induction motor (LIM) servo drive for periodic motion in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an on-line training RFNN with backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. In addition, a real-time GA is developed to search the optimal weights between the membership layer and the rule layer of RFNN on-line. The theoretical analyses for the proposed RFNN using GA controller are described in detail. Finally, experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance

关键词： Fuzzy neural networks Genetic algorithms Induction motors Servomechanisms Fuzzy control Motion control backpropagation algorithms Convergence Error analysis Lyapunov method

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A neuro-genetic algorithm approach for solving the inverse kinematics of robotic manipulators

A neuro-genetic algorithm approach for solving the inverse k...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： P. Karlra N.R. Prakash Dept. of Production Eng. Punjab Eng. Coll. Chandigarh India Department of Electronics Engineering Punjab Engineering College Chandigarh India

The inverse kinematics solution of a robotic manipulator requires the solution of non-linear equations having transcendental functions and involving time-consuming calculations. Artificial neural networks with their massively parallel architecture are natural candidates for providing a solution to this problem. In this work, a neuro-genetic algorithm approach is used to obtain the inverse kinematics solution of a robotic manipulator. A multi-layered feed-forward neural network architecture is used. The weights of the neural network are obtained during the training phase using a real-coded genetic algorithm. This training algorithm does not suffer from the usual drawbacks of the backpropagation learning algorithm. The approach is used to obtain the inverse kinematics solution of a planar robotic manipulator.

关键词： Kinematics Robots Manipulators Artificial neural networks backpropagation algorithms Nonlinear equations Parallel architectures Feedforward systems Neural networks Multi-layer neural network

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An artificial neural network algorithm and time series for improved forecasting of oil estimation: A case study of south korea and united kingdom (2001-2008)

An artificial neural network algorithm and time series for i...

引用

Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST

作者： V. Nadimi A. Azadeh M. Saberi S. Fattahi B. Danesh A. Tajvidi Department of Electrical Engineering Islamic Azad University of Tafresh Iran Department of Industrial Engineering College of Engineering University of Tehran Iran Department of Industrial Engineering University of Tafresh Tafresh Iran

This paper presents an Artificial Neural Network (ANN) algorithm to improve oil production forecasting. ANN algorithm is developed by different data preprocessing methods and considering different training algorithms and transfer functions in ANN models. Bayesian regularization backpropagation (BR), Levenberg-Marquardt back propagation (LM) and Gradient descent with momentum and adaptive learning rate backpropagation (GDX) are used as training algorithms. Also, log-sigmoid and Hyperbolic tangent sigmoid are used as transfer functions. 240 ANN in 6 groups are examined with one to forthy neuron in hidden layer. The efficiency of constructed ANN models is examined in South Korea via mean absolute percentage error (MAPE). One of feature of the proposed algorithm is utilization of Autocorrelation Function (ACF) to define input variables whereas conventional methods use trial and error method. Monthly oil production in South Korea January 2001 to July 2008 is considered as the case of this study.

关键词： Artificial neural networks Petroleum backpropagation algorithms Production Transfer functions Data preprocessing Bayesian methods Neurons Autocorrelation Input variables

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